next_steps/core_use_cases/related_items/personalize_aws_similar_items_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40d23571",
   "metadata": {},
   "source": [
    "# Amazon Personalize: using item metadata to identify stronger similarities\n",
    "\n",
    "The relevance of the recommendations you deliver with Amazon Personalize depends on the data available when the recommendations are generated. Amazon Personalize uses your users’ historical interactions, the attributes of your items, and your users’ metadata to learn what items are most relevant for each user. The primary data required by Amazon Personalize is user-item interactions. The interactions users have with items in your catalog, such as clicking on a product, reading an article, watching a video, or purchasing a product, are an important signal of what they have found relevant in the past. Including item and user attributes, also known as metadata, can enhance the relevance of recommendations; especially for new items that are similar to what your users have found relevant. However, structured metadata such as an item’s category, style, or genre may not always be readily available or doesn’t provide all the information that you have in your narrative descriptions. \n",
    "\n",
    "Now Amazon Personalize allows you to add item metadata such as product descriptions, genre of a movie, etc.. Amazon Personalize hosts, manages, and automatically processes your item metadata attributes and use it to improve the performance of your Amazon Personalize related items solutions.\n",
    "\n",
    "This notebook will demonstrate how the new recipe `aws-similar-items` plus item metadata improves the relevance of recommendations compared to the `SIMS` recipe.\n",
    "\n",
    "Amazon Reviews data from the Amazon Prime Pantry category are used for the interactions and items datasets.\n",
    "\n",
    "When considering including text in your items dataset, keep the following best practices in mind.\n",
    "\n",
    "One dataset group will be created that will include the interactions data plus item metadata this way we can train two separate models and compare their offline and online results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0def2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import boto3\n",
    "import time\n",
    "from time import sleep\n",
    "from lxml import html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc989f6",
   "metadata": {},
   "source": [
    "## Load and inspect datasets\n",
    "\n",
    "We'll start by loading the Prime Pantry reviews dataset. You will need to fill out the form for access to the data files:\n",
    "\n",
    "https://jmcauley.ucsd.edu/data/amazon/\n",
    "\n",
    "Citation:\n",
    "> Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering\n",
    "> R. He, J. McAuley\n",
    "> WWW, 2016\n",
    "> [pdf](http://cseweb.ucsd.edu/~jmcauley/pdfs/www16a.pdf)\n",
    "\n",
    "> Image-based recommendations on styles and substitutes\n",
    "> J. McAuley, C. Targett, J. Shi, A. van den Hengel\n",
    "> SIGIR, 2015\n",
    "> [pdf](http://cseweb.ucsd.edu/~jmcauley/pdfs/sigir15.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44c1e452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘raw_data’: File exists\n",
      "--2022-12-20 18:38:17--  http://jmcauley.ucsd.edu/data/amazon_v2/categoryFiles/Prime_Pantry.json.gz\n",
      "Resolving jmcauley.ucsd.edu (jmcauley.ucsd.edu)... 137.110.160.73\n",
      "Connecting to jmcauley.ucsd.edu (jmcauley.ucsd.edu)|137.110.160.73|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 45435146 (43M) [application/x-gzip]\n",
      "Saving to: ‘Prime_Pantry.json.gz’\n",
      "\n",
      "100%[======================================>] 45,435,146  23.6MB/s   in 1.8s   \n",
      "\n",
      "2022-12-20 18:38:19 (23.6 MB/s) - ‘Prime_Pantry.json.gz’ saved [45435146/45435146]\n",
      "\n",
      "--2022-12-20 18:38:19--  http://jmcauley.ucsd.edu/data/amazon_v2/metaFiles2/meta_Prime_Pantry.json.gz\n",
      "Resolving jmcauley.ucsd.edu (jmcauley.ucsd.edu)... 137.110.160.73\n",
      "Connecting to jmcauley.ucsd.edu (jmcauley.ucsd.edu)|137.110.160.73|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5281662 (5.0M) [application/x-gzip]\n",
      "Saving to: ‘meta_Prime_Pantry.json.gz’\n",
      "\n",
      "100%[======================================>] 5,281,662   6.53MB/s   in 0.8s   \n",
      "\n",
      "2022-12-20 18:38:20 (6.53 MB/s) - ‘meta_Prime_Pantry.json.gz’ saved [5281662/5281662]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_dir = 'raw_data'\n",
    "!mkdir $data_dir\n",
    "\n",
    "!cd $data_dir && \\\n",
    "    wget --tries=3 http://jmcauley.ucsd.edu/data/amazon_v2/categoryFiles/Prime_Pantry.json.gz && \\\n",
    "    wget --tries=3 http://jmcauley.ucsd.edu/data/amazon_v2/metaFiles2/meta_Prime_Pantry.json.gz\n",
    "\n",
    "        \n",
    "#wget http://deepyeti.ucsd.edu/jianmo/amazon/categoryFiles/Prime_Pantry.json.gz && \\\n",
    "#wget http://deepyeti.ucsd.edu/jianmo/amazon/metaFiles2/meta_Prime_Pantry.json.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b2c820",
   "metadata": {},
   "source": [
    "### Load and inspect reviews data\n",
    "\n",
    "We'll start by loading the reviews dataset for the Prime Pantry products and running some commands to see what we have to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48aa2063",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>overall</th>\n",
       "      <th>verified</th>\n",
       "      <th>reviewTime</th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>reviewerName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "      <th>unixReviewTime</th>\n",
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       "      <td>5</td>\n",
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       "      <td>12 14, 2014</td>\n",
       "      <td>A1NKJW0TNRVS7O</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Tamara M.</td>\n",
       "      <td>Good clinging</td>\n",
       "      <td>Clings  well</td>\n",
       "      <td>1418515200</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>A2L6X37E8TFTCC</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Amazon Customer</td>\n",
       "      <td>Fantastic buy and a good plastic wrap.  Even t...</td>\n",
       "      <td>Saran could use more Plus to Cling better.</td>\n",
       "      <td>1416441600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>noname</td>\n",
       "      <td>ok</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>1412985600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>09 1, 2014</td>\n",
       "      <td>A27EE7X7L29UMU</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>ZapNZs</td>\n",
       "      <td>Saran Cling Plus is kind of like most of the C...</td>\n",
       "      <td>The wrap is fantastic, but the dispensing, cut...</td>\n",
       "      <td>1409529600</td>\n",
       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>08 10, 2014</td>\n",
       "      <td>A1OWT4YZGB5GV9</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Amy Rogers</td>\n",
       "      <td>This is my go to plastic wrap so there isn't m...</td>\n",
       "      <td>has been doing it's job for years</td>\n",
       "      <td>1407628800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   overall  verified   reviewTime      reviewerID        asin  \\\n",
       "0        5      True  12 14, 2014  A1NKJW0TNRVS7O  B0000DIWNZ   \n",
       "1        4      True  11 20, 2014  A2L6X37E8TFTCC  B0000DIWNZ   \n",
       "2        4      True  10 11, 2014  A2WPR4W6V48121  B0000DIWNZ   \n",
       "3        3     False   09 1, 2014  A27EE7X7L29UMU  B0000DIWNZ   \n",
       "4        4      True  08 10, 2014  A1OWT4YZGB5GV9  B0000DIWNZ   \n",
       "\n",
       "      reviewerName                                         reviewText  \\\n",
       "0        Tamara M.                                      Good clinging   \n",
       "1  Amazon Customer  Fantastic buy and a good plastic wrap.  Even t...   \n",
       "2           noname                                                 ok   \n",
       "3           ZapNZs  Saran Cling Plus is kind of like most of the C...   \n",
       "4       Amy Rogers  This is my go to plastic wrap so there isn't m...   \n",
       "\n",
       "                                             summary  unixReviewTime vote  \\\n",
       "0                                       Clings  well      1418515200  NaN   \n",
       "1         Saran could use more Plus to Cling better.      1416441600  NaN   \n",
       "2                                         Four Stars      1412985600  NaN   \n",
       "3  The wrap is fantastic, but the dispensing, cut...      1409529600    4   \n",
       "4                  has been doing it's job for years      1407628800  NaN   \n",
       "\n",
       "  image style  \n",
       "0   NaN   NaN  \n",
       "1   NaN   NaN  \n",
       "2   NaN   NaN  \n",
       "3   NaN   NaN  \n",
       "4   NaN   NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pantry_df = pd.read_json(data_dir + '/Prime_Pantry.json.gz', lines=True, compression='infer')\n",
    "pantry_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fbb365e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(471614, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pantry_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b0ee1b",
   "metadata": {},
   "source": [
    "What can we learn from this output? There are over 471K reviews and 12 columns of data. The `asin` column is our unique item identifier, `reviewerID` is our unique user identifier, `unixReviewTime` is our timestamp for the review, and `overall` indicates the positivity of the review on a scale of 1-5. We will use this file as the basis for our interactions dataset for Personalize. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13c97c6",
   "metadata": {},
   "source": [
    "### Build and save interactions dataset\n",
    "\n",
    "Let's start building our interactions dataset by narrowing down the rows we want to include. The first step is to isolate only the positive reviews. For this we will assume any reviews with an overall rating of 4 or higher is a positive review. Anything rating of 3 or below are either mediocre or negative reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "279ef1cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(387692, 12)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews_df = pantry_df[pantry_df['overall'] > 3]\n",
    "positive_reviews_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5b43dd",
   "metadata": {},
   "source": [
    "We're down to 387K positive reviews. Still plenty for training a model in Personalize.\n",
    "\n",
    "Next let's narrow down the dataset to just the columns we need and add an `EVENT_TYPE` column to indicate the type of events we're capturing. Adding an `EVENT_TYPE` column now will make it easier to explore testing real-time events later if you choose to do so (since `eventType` is a required field for the [PutEvents](https://docs.aws.amazon.com/personalize/latest/dg/API_UBS_PutEvents.html) API)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8c6dff70",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>reviewerID</th>\n",
       "      <th>asin</th>\n",
       "      <th>unixReviewTime</th>\n",
       "      <th>overall</th>\n",
       "      <th>EVENT_TYPE</th>\n",
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       "      <td>A1NKJW0TNRVS7O</td>\n",
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       "      <th>4</th>\n",
       "      <td>A1OWT4YZGB5GV9</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1407628800</td>\n",
       "      <td>4</td>\n",
       "      <td>reviewed</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A1GN2ADKF1IE7K</td>\n",
       "      <td>B0000DIWNZ</td>\n",
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      ],
      "text/plain": [
       "       reviewerID        asin  unixReviewTime  overall EVENT_TYPE\n",
       "0  A1NKJW0TNRVS7O  B0000DIWNZ      1418515200        5   reviewed\n",
       "1  A2L6X37E8TFTCC  B0000DIWNZ      1416441600        4   reviewed\n",
       "2  A2WPR4W6V48121  B0000DIWNZ      1412985600        4   reviewed\n",
       "4  A1OWT4YZGB5GV9  B0000DIWNZ      1407628800        4   reviewed\n",
       "5  A1GN2ADKF1IE7K  B0000DIWNZ      1405296000        5   reviewed"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews_df = positive_reviews_df[['reviewerID', 'asin', 'unixReviewTime', 'overall']]\n",
    "positive_reviews_df['EVENT_TYPE']='reviewed'\n",
    "\n",
    "positive_reviews_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f1c8bf",
   "metadata": {},
   "source": [
    "One last check we should do is sanity check a `unixReviewTime` column value. Since Personalize builds sequence models based on the date and time of each interaction, it's important that the timestamp of each interaction is represented in the expected format so that it is interpreted correctly.\n",
    "\n",
    "Let's pick a value for the `unixReviewTime` column and parse it into a human-readable date so we can verify that it's reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85959397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1321488000\n",
      "2011-11-17 00:00:00\n"
     ]
    }
   ],
   "source": [
    "time_stamp = positive_reviews_df.iloc[50]['unixReviewTime']\n",
    "print(time_stamp)\n",
    "print(datetime.utcfromtimestamp(time_stamp).strftime('%Y-%m-%d %H:%M:%S'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e0ccf8",
   "metadata": {},
   "source": [
    "The timestamp value looks good. Let's get some final summary information for our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f998b8ea",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>unixReviewTime</th>\n",
       "      <th>overall</th>\n",
       "      <th>EVENT_TYPE</th>\n",
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       "      <td>387692</td>\n",
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       "      <th>unique</th>\n",
       "      <td>202254</td>\n",
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       "      <td>reviewed</td>\n",
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       "      <th>freq</th>\n",
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       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.468847e+09</td>\n",
       "      <td>4.847227</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.314975e+07</td>\n",
       "      <td>0.359769</td>\n",
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       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.073693e+09</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.447200e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.474718e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.498435e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.538611e+09</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            reviewerID        asin  unixReviewTime        overall EVENT_TYPE\n",
       "count           387692      387692    3.876920e+05  387692.000000     387692\n",
       "unique          202254       10584             NaN            NaN          1\n",
       "top     A35Q0RBM3YNQNF  B00XA9DADC             NaN            NaN   reviewed\n",
       "freq               176        5288             NaN            NaN     387692\n",
       "mean               NaN         NaN    1.468847e+09       4.847227        NaN\n",
       "std                NaN         NaN    4.314975e+07       0.359769        NaN\n",
       "min                NaN         NaN    1.073693e+09       4.000000        NaN\n",
       "25%                NaN         NaN    1.447200e+09       5.000000        NaN\n",
       "50%                NaN         NaN    1.474718e+09       5.000000        NaN\n",
       "75%                NaN         NaN    1.498435e+09       5.000000        NaN\n",
       "max                NaN         NaN    1.538611e+09       5.000000        NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews_df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78b4f171",
   "metadata": {},
   "source": [
    "We have 387K reviews for 202K distinct reviewers/users across 10K unique products. This is basis of our interactions dataset.\n",
    "\n",
    "Before we can use this as our interactions dataset, though, we need to rename the columns to match those expected by Personalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "468897a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USER_ID</th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>TIMESTAMP</th>\n",
       "      <th>EVENT_VALUE</th>\n",
       "      <th>EVENT_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1NKJW0TNRVS7O</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1418515200</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A2L6X37E8TFTCC</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1416441600</td>\n",
       "      <td>4</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2WPR4W6V48121</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1412985600</td>\n",
       "      <td>4</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1OWT4YZGB5GV9</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1407628800</td>\n",
       "      <td>4</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A1GN2ADKF1IE7K</td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>1405296000</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471609</th>\n",
       "      <td>A19GSVHXVT5NNF</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>1494892800</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471610</th>\n",
       "      <td>ABSCTKLX9F9IU</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>1493769600</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471611</th>\n",
       "      <td>A2R33RCWKDHZ3L</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>1492646400</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471612</th>\n",
       "      <td>A2INGHYEXZDHMC</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>1492560000</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471613</th>\n",
       "      <td>AJ8M6GVFGIRLS</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>1487808000</td>\n",
       "      <td>5</td>\n",
       "      <td>reviewed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>387692 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               USER_ID     ITEM_ID   TIMESTAMP  EVENT_VALUE EVENT_TYPE\n",
       "0       A1NKJW0TNRVS7O  B0000DIWNZ  1418515200            5   reviewed\n",
       "1       A2L6X37E8TFTCC  B0000DIWNZ  1416441600            4   reviewed\n",
       "2       A2WPR4W6V48121  B0000DIWNZ  1412985600            4   reviewed\n",
       "4       A1OWT4YZGB5GV9  B0000DIWNZ  1407628800            4   reviewed\n",
       "5       A1GN2ADKF1IE7K  B0000DIWNZ  1405296000            5   reviewed\n",
       "...                ...         ...         ...          ...        ...\n",
       "471609  A19GSVHXVT5NNF  B01HI8JVI8  1494892800            5   reviewed\n",
       "471610   ABSCTKLX9F9IU  B01HI8JVI8  1493769600            5   reviewed\n",
       "471611  A2R33RCWKDHZ3L  B01HI8JVI8  1492646400            5   reviewed\n",
       "471612  A2INGHYEXZDHMC  B01HI8JVI8  1492560000            5   reviewed\n",
       "471613   AJ8M6GVFGIRLS  B01HI8JVI8  1487808000            5   reviewed\n",
       "\n",
       "[387692 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_reviews_df.rename(columns = {'reviewerID':'USER_ID', 'asin':'ITEM_ID', \n",
    "                              'unixReviewTime':'TIMESTAMP', 'overall': 'EVENT_VALUE'}, inplace = True)\n",
    "positive_reviews_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93092b3",
   "metadata": {},
   "source": [
    "Finally, let's save our positive reviews dataframe as a CSV. We'll upload this CSV to Personalize later in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e2bdc8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "interactions_filename = \"interactions.csv\"\n",
    "positive_reviews_df.to_csv(interactions_filename, index=False, float_format='%.0f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61836a57",
   "metadata": {},
   "source": [
    "### Load and inspect item metadata\n",
    "\n",
    "Now that we have the interactions dataset established, let's turn to the items dataset. This is where we will find the unstructured text value that we will include in the model.\n",
    "\n",
    "Like the reviews dataset, the Prime Pantry item metadata file is also represented in JSON. Due to the nested nature of this file, this will present some challenges in getting our data formatted the way we need it.\n",
    "\n",
    "Let's start by loading the metadata file into a dataframe and taking a look the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a372a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>tech1</th>\n",
       "      <th>description</th>\n",
       "      <th>fit</th>\n",
       "      <th>title</th>\n",
       "      <th>also_buy</th>\n",
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       "      <th>similar_item</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>asin</th>\n",
       "      <th>imageURL</th>\n",
       "      <th>imageURLHighRes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Sink your sweet tooth into MILK DUDS Candya d...</td>\n",
       "      <td></td>\n",
       "      <td>HERSHEY'S Milk Duds Candy, 5 Ounce(Halloween C...</td>\n",
       "      <td>[B019KE37WO, B007NQSWEU]</td>\n",
       "      <td></td>\n",
       "      <td>Milk Duds</td>\n",
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       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'ASIN: ': 'B00005BPJO', 'Item model number:':...</td>\n",
       "      <td>&lt;img src=\"https://m.media-amazon.com/images/G/...</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$5.00</td>\n",
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       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[]</td>\n",
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       "      <td>[Sink your sweet tooth into MILK DUDS Candya d...</td>\n",
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       "      <td>HERSHEY'S Milk Duds Candy, 5 Ounce(Halloween C...</td>\n",
       "      <td>[B019KE37WO, B007NQSWEU]</td>\n",
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       "      <td>Milk Duds</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[A perfect Lentil soup starts with Goya Lentil...</td>\n",
       "      <td></td>\n",
       "      <td>Goya Dry Lentils, 16 oz</td>\n",
       "      <td>[B003SI144W, B000VDRKEK]</td>\n",
       "      <td></td>\n",
       "      <td>Goya</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[B074MFVZG7, B079PTH69L, B000VDRKEK, B074M9T81...</td>\n",
       "      <td>{'ASIN: ': 'B0000DIF38'}</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
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       "      <td>NaT</td>\n",
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       "      <td>B0000DIF38</td>\n",
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       "      <td></td>\n",
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       "      <td></td>\n",
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       "      <td>[B01MY5FHT6, B000PYF8VM, B000SRMDFA, B07CX6LN8...</td>\n",
       "      <td></td>\n",
       "      <td>Saran</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
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       "      <td>{'Domestic Shipping: ': 'This item can only be...</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>B0000DIWNI</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
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       "      <td></td>\n",
       "      <td>Saran Cling Plus Plastic Wrap, 200 Sq Ft</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>Saran</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[B0014CZ0TE]</td>\n",
       "      <td>{'Domestic Shipping: ': 'This item can only be...</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
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       "      <td>NaT</td>\n",
       "      <td></td>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10808</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[These bars are where our journey started and ...</td>\n",
       "      <td></td>\n",
       "      <td>KIND Bars, Caramel Almond &amp;amp; Sea Salt, Glut...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>KIND</td>\n",
       "      <td>[]</td>\n",
       "      <td>26,259 in Grocery &amp; Gourmet Food (</td>\n",
       "      <td>[B00JQQAN60, B00JQQAWSY, B0111K7V54, B0111K8L9...</td>\n",
       "      <td>{'ASIN: ': 'B01HI76312', 'Item model number:':...</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$3.98</td>\n",
       "      <td>B01HI76312</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[These bars are where our journey started and ...</td>\n",
       "      <td></td>\n",
       "      <td>KIND Bars, Maple Glazed Pecan &amp;amp; Sea Salt, ...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>KIND</td>\n",
       "      <td>[]</td>\n",
       "      <td>16,822 in Grocery &amp; Gourmet Food (</td>\n",
       "      <td>[B0111K97JC, B00JQQAN60, B0111K8L9Y, B01HI7631...</td>\n",
       "      <td>{'ASIN: ': 'B01HI76790'}</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$5.81</td>\n",
       "      <td>B01HI76790</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10810</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[These bars are where our journey started and ...</td>\n",
       "      <td></td>\n",
       "      <td>KIND Bars, Dark Chocolate Almond &amp;amp; Coconut...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>KIND</td>\n",
       "      <td>[]</td>\n",
       "      <td>107,057 in Grocery &amp; Gourmet Food (</td>\n",
       "      <td>[B0111K7V54, B01HI76312, B00JQQAL0S, B0111K97J...</td>\n",
       "      <td>{'ASIN: ': 'B01HI76SA8'}</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$4.98</td>\n",
       "      <td>B01HI76SA8</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[These bars are where our journey started and ...</td>\n",
       "      <td></td>\n",
       "      <td>KIND Bars, Honey Roasted Nuts &amp;amp; Sea Salt, ...</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>KIND</td>\n",
       "      <td>[]</td>\n",
       "      <td>24,648 in Grocery &amp; Gourmet Food (</td>\n",
       "      <td>[B00JQQAN60, B0111K7V54, B01HI76312, B0111K97J...</td>\n",
       "      <td>{'ASIN: ': 'B01HI76XS0'}</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$5.81</td>\n",
       "      <td>B01HI76XS0</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "      <td>[https://images-na.ssl-images-amazon.com/image...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>[Delicious, crowd pleasing and convenient it c...</td>\n",
       "      <td></td>\n",
       "      <td>Tostitos Salsa, Mild, 69 Ounce</td>\n",
       "      <td>[B00ID0EBP4, B00ID0EB1S, B00N0WZSOS, B00IAE65L...</td>\n",
       "      <td></td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>[]</td>\n",
       "      <td>259,965 in Grocery &amp; Gourmet Food (</td>\n",
       "      <td>[]</td>\n",
       "      <td>{'ASIN: ': 'B01HI8JVI8'}</td>\n",
       "      <td>&lt;img src=\"https://images-na.ssl-images-amazon....</td>\n",
       "      <td></td>\n",
       "      <td>NaT</td>\n",
       "      <td>$6.82</td>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10813 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category tech1                                        description fit  \\\n",
       "0           []        [Sink your sweet tooth into MILK DUDS Candya d...       \n",
       "1           []        [Sink your sweet tooth into MILK DUDS Candya d...       \n",
       "2           []        [A perfect Lentil soup starts with Goya Lentil...       \n",
       "3           []        [Saran Premium Wrap is an extra tough yet easy...       \n",
       "4           []        [200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Eas...       \n",
       "...        ...   ...                                                ...  ..   \n",
       "10808       []        [These bars are where our journey started and ...       \n",
       "10809       []        [These bars are where our journey started and ...       \n",
       "10810       []        [These bars are where our journey started and ...       \n",
       "10811       []        [These bars are where our journey started and ...       \n",
       "10812       []        [Delicious, crowd pleasing and convenient it c...       \n",
       "\n",
       "                                                   title  \\\n",
       "0      HERSHEY'S Milk Duds Candy, 5 Ounce(Halloween C...   \n",
       "1      HERSHEY'S Milk Duds Candy, 5 Ounce(Halloween C...   \n",
       "2                                Goya Dry Lentils, 16 oz   \n",
       "3                  Saran Premium Plastic Wrap, 100 Sq Ft   \n",
       "4               Saran Cling Plus Plastic Wrap, 200 Sq Ft   \n",
       "...                                                  ...   \n",
       "10808  KIND Bars, Caramel Almond &amp; Sea Salt, Glut...   \n",
       "10809  KIND Bars, Maple Glazed Pecan &amp; Sea Salt, ...   \n",
       "10810  KIND Bars, Dark Chocolate Almond &amp; Coconut...   \n",
       "10811  KIND Bars, Honey Roasted Nuts &amp; Sea Salt, ...   \n",
       "10812                     Tostitos Salsa, Mild, 69 Ounce   \n",
       "\n",
       "                                                also_buy tech2      brand  \\\n",
       "0                               [B019KE37WO, B007NQSWEU]        Milk Duds   \n",
       "1                               [B019KE37WO, B007NQSWEU]        Milk Duds   \n",
       "2                               [B003SI144W, B000VDRKEK]             Goya   \n",
       "3      [B01MY5FHT6, B000PYF8VM, B000SRMDFA, B07CX6LN8...            Saran   \n",
       "4                                                     []            Saran   \n",
       "...                                                  ...   ...        ...   \n",
       "10808                                                 []             KIND   \n",
       "10809                                                 []             KIND   \n",
       "10810                                                 []             KIND   \n",
       "10811                                                 []             KIND   \n",
       "10812  [B00ID0EBP4, B00ID0EB1S, B00N0WZSOS, B00IAE65L...         Tostitos   \n",
       "\n",
       "      feature                                 rank  \\\n",
       "0          []                                   []   \n",
       "1          []                                   []   \n",
       "2          []                                   []   \n",
       "3          []                                   []   \n",
       "4          []                                   []   \n",
       "...       ...                                  ...   \n",
       "10808      []   26,259 in Grocery & Gourmet Food (   \n",
       "10809      []   16,822 in Grocery & Gourmet Food (   \n",
       "10810      []  107,057 in Grocery & Gourmet Food (   \n",
       "10811      []   24,648 in Grocery & Gourmet Food (   \n",
       "10812      []  259,965 in Grocery & Gourmet Food (   \n",
       "\n",
       "                                               also_view  \\\n",
       "0                                                     []   \n",
       "1                                                     []   \n",
       "2      [B074MFVZG7, B079PTH69L, B000VDRKEK, B074M9T81...   \n",
       "3      [B077QLSLRQ, B00JPKW1RQ, B000FE2IK6, B00XUJHJ9...   \n",
       "4                                           [B0014CZ0TE]   \n",
       "...                                                  ...   \n",
       "10808  [B00JQQAN60, B00JQQAWSY, B0111K7V54, B0111K8L9...   \n",
       "10809  [B0111K97JC, B00JQQAN60, B0111K8L9Y, B01HI7631...   \n",
       "10810  [B0111K7V54, B01HI76312, B00JQQAL0S, B0111K97J...   \n",
       "10811  [B00JQQAN60, B0111K7V54, B01HI76312, B0111K97J...   \n",
       "10812                                                 []   \n",
       "\n",
       "                                                 details  \\\n",
       "0      {'ASIN: ': 'B00005BPJO', 'Item model number:':...   \n",
       "1      {'ASIN: ': 'B00005BPJO', 'Item model number:':...   \n",
       "2                               {'ASIN: ': 'B0000DIF38'}   \n",
       "3      {'Domestic Shipping: ': 'This item can only be...   \n",
       "4      {'Domestic Shipping: ': 'This item can only be...   \n",
       "...                                                  ...   \n",
       "10808  {'ASIN: ': 'B01HI76312', 'Item model number:':...   \n",
       "10809                           {'ASIN: ': 'B01HI76790'}   \n",
       "10810                           {'ASIN: ': 'B01HI76SA8'}   \n",
       "10811                           {'ASIN: ': 'B01HI76XS0'}   \n",
       "10812                           {'ASIN: ': 'B01HI8JVI8'}   \n",
       "\n",
       "                                                main_cat similar_item date  \\\n",
       "0      <img src=\"https://m.media-amazon.com/images/G/...               NaT   \n",
       "1      <img src=\"https://m.media-amazon.com/images/G/...               NaT   \n",
       "2      <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "3      <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "4      <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "...                                                  ...          ...  ...   \n",
       "10808  <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "10809  <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "10810  <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "10811  <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "10812  <img src=\"https://images-na.ssl-images-amazon....               NaT   \n",
       "\n",
       "       price        asin                                           imageURL  \\\n",
       "0      $5.00  B00005BPJO  [https://images-na.ssl-images-amazon.com/image...   \n",
       "1      $5.00  B00005BPJO  [https://images-na.ssl-images-amazon.com/image...   \n",
       "2             B0000DIF38  [https://images-na.ssl-images-amazon.com/image...   \n",
       "3             B0000DIWNI  [https://images-na.ssl-images-amazon.com/image...   \n",
       "4             B0000DIWNZ  [https://images-na.ssl-images-amazon.com/image...   \n",
       "...      ...         ...                                                ...   \n",
       "10808  $3.98  B01HI76312  [https://images-na.ssl-images-amazon.com/image...   \n",
       "10809  $5.81  B01HI76790  [https://images-na.ssl-images-amazon.com/image...   \n",
       "10810  $4.98  B01HI76SA8                                                 []   \n",
       "10811  $5.81  B01HI76XS0  [https://images-na.ssl-images-amazon.com/image...   \n",
       "10812  $6.82  B01HI8JVI8                                                 []   \n",
       "\n",
       "                                         imageURLHighRes  \n",
       "0      [https://images-na.ssl-images-amazon.com/image...  \n",
       "1      [https://images-na.ssl-images-amazon.com/image...  \n",
       "2      [https://images-na.ssl-images-amazon.com/image...  \n",
       "3      [https://images-na.ssl-images-amazon.com/image...  \n",
       "4      [https://images-na.ssl-images-amazon.com/image...  \n",
       "...                                                  ...  \n",
       "10808  [https://images-na.ssl-images-amazon.com/image...  \n",
       "10809  [https://images-na.ssl-images-amazon.com/image...  \n",
       "10810                                                 []  \n",
       "10811  [https://images-na.ssl-images-amazon.com/image...  \n",
       "10812                                                 []  \n",
       "\n",
       "[10813 rows x 19 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pantry_meta_df = pd.read_json('raw_data/meta_Prime_Pantry.json.gz', lines=True, compression='infer')\n",
    "pantry_meta_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c067198",
   "metadata": {},
   "source": [
    "So what can we learn from this information? First, there are over 10K products represented in the metadata file. Most of the columns will be of little value to us for Personalize since they aren't relevant as features (image URLs, `details`, `also_viewed`, `also_buy`, etc) or are mostly blank/sparse (`category`, `fit`, `tech1`, etc). The `asin` column is our unique identifier for each item (although there looks to be one duplicate) and `brand` and `price` look like they may be useful. The `description` column is what we will use for unstructured text.\n",
    "\n",
    "However, we have to do some cleanup and reformatting of the fields we want to use in our items dataset. For example, the `price` field is a formatted currency value (string) and not numeric and the `description` field was loaded as an array of strings due to how they values were represented and parsed from the original JSON file. Lastly, the `description` values also contain HTML markup that needs to stripped.\n",
    "\n",
    "Let's start by creating a dataframe with just the columns we need for the items dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "351123cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00005BPJO</td>\n",
       "      <td>Milk Duds</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>[Sink your sweet tooth into MILK DUDS Candya d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00005BPJO</td>\n",
       "      <td>Milk Duds</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>[Sink your sweet tooth into MILK DUDS Candya d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0000DIF38</td>\n",
       "      <td>Goya</td>\n",
       "      <td></td>\n",
       "      <td>[A perfect Lentil soup starts with Goya Lentil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0000DIWNI</td>\n",
       "      <td>Saran</td>\n",
       "      <td></td>\n",
       "      <td>[Saran Premium Wrap is an extra tough yet easy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Saran</td>\n",
       "      <td></td>\n",
       "      <td>[200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Eas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B0000GH6UG</td>\n",
       "      <td>Ibarra</td>\n",
       "      <td></td>\n",
       "      <td>[Ibarra Chocolate, 19 Oz, , ]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B0000KC2BK</td>\n",
       "      <td>Knorr</td>\n",
       "      <td>$3.09</td>\n",
       "      <td>[Knorr Granulated Chicken Flavor Bouillon is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B0001E1IN8</td>\n",
       "      <td>Castillo</td>\n",
       "      <td></td>\n",
       "      <td>[Red chili habanero sauces. They are present t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00032E8XK</td>\n",
       "      <td>Chicken of the Sea</td>\n",
       "      <td>$1.48</td>\n",
       "      <td>[Chicken of the Sea Solid White Albacore Tuna ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0005XMTHE</td>\n",
       "      <td>Smucker's</td>\n",
       "      <td>$2.29</td>\n",
       "      <td>[Helps build muscles with bcaa's amino acids, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         asin               brand  price  \\\n",
       "0  B00005BPJO           Milk Duds  $5.00   \n",
       "1  B00005BPJO           Milk Duds  $5.00   \n",
       "2  B0000DIF38                Goya          \n",
       "3  B0000DIWNI               Saran          \n",
       "4  B0000DIWNZ               Saran          \n",
       "5  B0000GH6UG              Ibarra          \n",
       "6  B0000KC2BK               Knorr  $3.09   \n",
       "7  B0001E1IN8            Castillo          \n",
       "8  B00032E8XK  Chicken of the Sea  $1.48   \n",
       "9  B0005XMTHE           Smucker's  $2.29   \n",
       "\n",
       "                                         description  \n",
       "0  [Sink your sweet tooth into MILK DUDS Candya d...  \n",
       "1  [Sink your sweet tooth into MILK DUDS Candya d...  \n",
       "2  [A perfect Lentil soup starts with Goya Lentil...  \n",
       "3  [Saran Premium Wrap is an extra tough yet easy...  \n",
       "4  [200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Eas...  \n",
       "5                      [Ibarra Chocolate, 19 Oz, , ]  \n",
       "6  [Knorr Granulated Chicken Flavor Bouillon is a...  \n",
       "7  [Red chili habanero sauces. They are present t...  \n",
       "8  [Chicken of the Sea Solid White Albacore Tuna ...  \n",
       "9  [Helps build muscles with bcaa's amino acids, ...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df = pantry_meta_df.copy()\n",
    "items_df = items_df[['asin', 'brand', 'price', 'description']]\n",
    "items_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f833f323",
   "metadata": {},
   "source": [
    "Next let's drop duplicate rows based on the `asin` column value (in case there are any)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f794b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10812, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df = items_df.drop_duplicates(subset=['asin'], keep='last')\n",
    "items_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc46efc8",
   "metadata": {},
   "source": [
    "Next let's focus on reformatting and cleaning up the `description` column values. As you can see above, the `description` is currently represented as an array of strings (because that's how it is represented in the JSON file). We need to flatten this array into a single string and strip all HTML markup from each fragment.\n",
    "\n",
    "We'll start by creating two utility functions that will be used to clean the `description` (and later the `title` column in the original dataset when we want to display titles for recommended products)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a45aa8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strips and cleans a value of HTML markup and whitespace.\n",
    "def clean_markup(value):\n",
    "    s = str(value).strip()\n",
    "    if s != '':\n",
    "        s = str(html.fromstring(s).text_content())\n",
    "        s = ' '.join(s.split())\n",
    "                \n",
    "    return s.strip()\n",
    "\n",
    "# Cleans and reformats the description column value for a dataframe row.\n",
    "def clean_and_reformat_description(row):\n",
    "    s = ''\n",
    "    for el in row['description']:\n",
    "        el = clean_markup(el)\n",
    "        if el != '':\n",
    "            s += ' ' + el\n",
    "                \n",
    "    return s.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "348dcda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00005BPJO</td>\n",
       "      <td>Milk Duds</td>\n",
       "      <td>$5.00</td>\n",
       "      <td>Sink your sweet tooth into MILK DUDS Candya de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0000DIF38</td>\n",
       "      <td>Goya</td>\n",
       "      <td></td>\n",
       "      <td>A perfect Lentil soup starts with Goya Lentils...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0000DIWNI</td>\n",
       "      <td>Saran</td>\n",
       "      <td></td>\n",
       "      <td>Saran Premium Wrap is an extra tough yet easy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Saran</td>\n",
       "      <td></td>\n",
       "      <td>200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B0000GH6UG</td>\n",
       "      <td>Ibarra</td>\n",
       "      <td></td>\n",
       "      <td>Ibarra Chocolate, 19 Oz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10808</th>\n",
       "      <td>B01HI76312</td>\n",
       "      <td>KIND</td>\n",
       "      <td>$3.98</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>B01HI76790</td>\n",
       "      <td>KIND</td>\n",
       "      <td>$5.81</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10810</th>\n",
       "      <td>B01HI76SA8</td>\n",
       "      <td>KIND</td>\n",
       "      <td>$4.98</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>B01HI76XS0</td>\n",
       "      <td>KIND</td>\n",
       "      <td>$5.81</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>$6.82</td>\n",
       "      <td>Delicious, crowd pleasing and convenient it ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10812 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             asin      brand  price  \\\n",
       "1      B00005BPJO  Milk Duds  $5.00   \n",
       "2      B0000DIF38       Goya          \n",
       "3      B0000DIWNI      Saran          \n",
       "4      B0000DIWNZ      Saran          \n",
       "5      B0000GH6UG     Ibarra          \n",
       "...           ...        ...    ...   \n",
       "10808  B01HI76312       KIND  $3.98   \n",
       "10809  B01HI76790       KIND  $5.81   \n",
       "10810  B01HI76SA8       KIND  $4.98   \n",
       "10811  B01HI76XS0       KIND  $5.81   \n",
       "10812  B01HI8JVI8   Tostitos  $6.82   \n",
       "\n",
       "                                             description  \n",
       "1      Sink your sweet tooth into MILK DUDS Candya de...  \n",
       "2      A perfect Lentil soup starts with Goya Lentils...  \n",
       "3      Saran Premium Wrap is an extra tough yet easy ...  \n",
       "4      200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...  \n",
       "5                                Ibarra Chocolate, 19 Oz  \n",
       "...                                                  ...  \n",
       "10808  These bars are where our journey started and i...  \n",
       "10809  These bars are where our journey started and i...  \n",
       "10810  These bars are where our journey started and i...  \n",
       "10811  These bars are where our journey started and i...  \n",
       "10812  Delicious, crowd pleasing and convenient it ca...  \n",
       "\n",
       "[10812 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df['description'] = items_df.apply(clean_and_reformat_description, axis=1)\n",
    "items_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c17e504e",
   "metadata": {},
   "source": [
    "Next let's take a look at the `price` column and change its type from a string to a float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1705b91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          4063\n",
       "$2.99      114\n",
       "$3.99      113\n",
       "$4.99      103\n",
       "$5.99       87\n",
       "          ... \n",
       "$12.93       1\n",
       "$20.80       1\n",
       "$13.97       1\n",
       "$11.46       1\n",
       "$4.32        1\n",
       "Name: price, Length: 1482, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df['price'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bcfbcc",
   "metadata": {},
   "source": [
    "The following cell with convert empty/non-numeric prices to `np.nan` and all others will have the `$` currency symbol removed. This will allow us to coerce the type to a float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d715a641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>brand</th>\n",
       "      <th>price</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00005BPJO</td>\n",
       "      <td>Milk Duds</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Sink your sweet tooth into MILK DUDS Candya de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0000DIF38</td>\n",
       "      <td>Goya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A perfect Lentil soup starts with Goya Lentils...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0000DIWNI</td>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saran Premium Wrap is an extra tough yet easy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B0000GH6UG</td>\n",
       "      <td>Ibarra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ibarra Chocolate, 19 Oz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10808</th>\n",
       "      <td>B01HI76312</td>\n",
       "      <td>KIND</td>\n",
       "      <td>3.98</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>B01HI76790</td>\n",
       "      <td>KIND</td>\n",
       "      <td>5.81</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10810</th>\n",
       "      <td>B01HI76SA8</td>\n",
       "      <td>KIND</td>\n",
       "      <td>4.98</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>B01HI76XS0</td>\n",
       "      <td>KIND</td>\n",
       "      <td>5.81</td>\n",
       "      <td>These bars are where our journey started and i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>B01HI8JVI8</td>\n",
       "      <td>Tostitos</td>\n",
       "      <td>6.82</td>\n",
       "      <td>Delicious, crowd pleasing and convenient it ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10812 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             asin      brand price  \\\n",
       "1      B00005BPJO  Milk Duds  5.00   \n",
       "2      B0000DIF38       Goya   NaN   \n",
       "3      B0000DIWNI      Saran   NaN   \n",
       "4      B0000DIWNZ      Saran   NaN   \n",
       "5      B0000GH6UG     Ibarra   NaN   \n",
       "...           ...        ...   ...   \n",
       "10808  B01HI76312       KIND  3.98   \n",
       "10809  B01HI76790       KIND  5.81   \n",
       "10810  B01HI76SA8       KIND  4.98   \n",
       "10811  B01HI76XS0       KIND  5.81   \n",
       "10812  B01HI8JVI8   Tostitos  6.82   \n",
       "\n",
       "                                             description  \n",
       "1      Sink your sweet tooth into MILK DUDS Candya de...  \n",
       "2      A perfect Lentil soup starts with Goya Lentils...  \n",
       "3      Saran Premium Wrap is an extra tough yet easy ...  \n",
       "4      200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...  \n",
       "5                                Ibarra Chocolate, 19 Oz  \n",
       "...                                                  ...  \n",
       "10808  These bars are where our journey started and i...  \n",
       "10809  These bars are where our journey started and i...  \n",
       "10810  These bars are where our journey started and i...  \n",
       "10811  These bars are where our journey started and i...  \n",
       "10812  Delicious, crowd pleasing and convenient it ca...  \n",
       "\n",
       "[10812 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_price(row):\n",
    "    v = str(row['price']).strip().replace('$', '')\n",
    "    if v == '' or not v.lstrip('-').replace('.', '').isdigit():\n",
    "        return np.nan\n",
    "    return v\n",
    "\n",
    "items_df['price'] = items_df.apply(convert_price, axis=1)\n",
    "items_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72bca69d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.99     114\n",
       "3.99     113\n",
       "4.99     103\n",
       "5.99      87\n",
       "2.98      76\n",
       "        ... \n",
       "7.62       1\n",
       "16.18      1\n",
       "21.84      1\n",
       "12.93      1\n",
       "4.32       1\n",
       "Name: price, Length: 1480, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df['price'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "426a65ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "items_df['price'] = items_df['price'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39a6e8a",
   "metadata": {},
   "source": [
    "Next we'll rename the columns to match the names and uppercase name format expected by Personalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90be07a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00005BPJO</td>\n",
       "      <td>Milk Duds</td>\n",
       "      <td>5.00</td>\n",
       "      <td>Sink your sweet tooth into MILK DUDS Candya de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0000DIF38</td>\n",
       "      <td>Goya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A perfect Lentil soup starts with Goya Lentils...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0000DIWNI</td>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saran Premium Wrap is an extra tough yet easy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0000DIWNZ</td>\n",
       "      <td>Saran</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B0000GH6UG</td>\n",
       "      <td>Ibarra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ibarra Chocolate, 19 Oz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B0000KC2BK</td>\n",
       "      <td>Knorr</td>\n",
       "      <td>3.09</td>\n",
       "      <td>Knorr Granulated Chicken Flavor Bouillon is a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B0001E1IN8</td>\n",
       "      <td>Castillo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Red chili habanero sauces. They are present to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00032E8XK</td>\n",
       "      <td>Chicken of the Sea</td>\n",
       "      <td>1.48</td>\n",
       "      <td>Chicken of the Sea Solid White Albacore Tuna i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0005XMTHE</td>\n",
       "      <td>Smucker's</td>\n",
       "      <td>2.29</td>\n",
       "      <td>Helps build muscles with bcaa's amino acids, i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B0005XNE6E</td>\n",
       "      <td>Snapple</td>\n",
       "      <td>1.99</td>\n",
       "      <td>At Snapple, we believe lifes a peach. Weve bee...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ITEM_ID               BRAND  PRICE  \\\n",
       "1   B00005BPJO           Milk Duds   5.00   \n",
       "2   B0000DIF38                Goya    NaN   \n",
       "3   B0000DIWNI               Saran    NaN   \n",
       "4   B0000DIWNZ               Saran    NaN   \n",
       "5   B0000GH6UG              Ibarra    NaN   \n",
       "6   B0000KC2BK               Knorr   3.09   \n",
       "7   B0001E1IN8            Castillo    NaN   \n",
       "8   B00032E8XK  Chicken of the Sea   1.48   \n",
       "9   B0005XMTHE           Smucker's   2.29   \n",
       "10  B0005XNE6E             Snapple   1.99   \n",
       "\n",
       "                                          DESCRIPTION  \n",
       "1   Sink your sweet tooth into MILK DUDS Candya de...  \n",
       "2   A perfect Lentil soup starts with Goya Lentils...  \n",
       "3   Saran Premium Wrap is an extra tough yet easy ...  \n",
       "4   200 sq ft (285 ft x 11-3/4 in x 18.6 m2). Easy...  \n",
       "5                             Ibarra Chocolate, 19 Oz  \n",
       "6   Knorr Granulated Chicken Flavor Bouillon is a ...  \n",
       "7   Red chili habanero sauces. They are present to...  \n",
       "8   Chicken of the Sea Solid White Albacore Tuna i...  \n",
       "9   Helps build muscles with bcaa's amino acids, i...  \n",
       "10  At Snapple, we believe lifes a peach. Weve bee...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df.rename(columns = {'asin':'ITEM_ID', 'brand':'BRAND', \n",
    "                              'price':'PRICE', 'description': 'DESCRIPTION'}, inplace = True)\n",
    "items_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2950515",
   "metadata": {},
   "source": [
    "We'll be creating one items CSV. We'll use this file to train our personalize models so we can compare the offline metrics and do some online inspection of recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d95e3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "items_filename = \"items-metadata.csv\"\n",
    "items_df.to_csv(items_filename, index=False, float_format='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a4ccb1",
   "metadata": {},
   "source": [
    "## Create dataset groups and upload datasets\n",
    "\n",
    "With the datasets that we need built, now it's time to upload them to Personalize using dataset import jobs. Before we can upload the CSVs, we need to create dataset groups, create schemas for our datasets, and create datasets.\n",
    "\n",
    "We'll start by creating SDK client that we'll need to interact with Personalize."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "409e94f8",
   "metadata": {},
   "source": [
    "### Create dataset groups\n",
    "\n",
    "Let's create our dataset group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70077c3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "personalize = boto3.client('personalize')\n",
    "personalize_runtime = boto3.client('personalize-runtime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3527f755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"datasetGroupArn\": \"arn:aws:personalize:us-east-1:700974296802:dataset-group/amazon-pantry-aws-similar-items\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"69c1e20c-46d5-4a1c-98fe-b62c98d360c9\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 18:58:32 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"110\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"69c1e20c-46d5-4a1c-98fe-b62c98d360c9\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "create_dataset_group_response = personalize.create_dataset_group(\n",
    "    name = \"amazon-pantry-aws-similar-items\"\n",
    ")\n",
    "\n",
    "dataset_group = create_dataset_group_response['datasetGroupArn']\n",
    "print(json.dumps(create_dataset_group_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12454abb",
   "metadata": {},
   "source": [
    "Since dataset groups can take a few seconds to be fully created, let's wait until they both have a status of ACTIVE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "16d91ef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one dataset group create is still in progress\n",
      "At least one dataset group create is still in progress\n",
      "Dataset group create succeeded for arn:aws:personalize:us-east-1:700974296802:dataset-group/amazon-pantry-aws-similar-items\n"
     ]
    }
   ],
   "source": [
    "in_progress_dataset_group_arns = [ dataset_group ]\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    for dataset_group_arn in in_progress_dataset_group_arns:\n",
    "        describe_dataset_group_response = personalize.describe_dataset_group(\n",
    "            datasetGroupArn = dataset_group_arn\n",
    "        )\n",
    "        status = describe_dataset_group_response[\"datasetGroup\"][\"status\"]\n",
    "        if status == \"ACTIVE\":\n",
    "            print(\"Dataset group create succeeded for {}\".format(dataset_group_arn))\n",
    "            in_progress_dataset_group_arns.remove(dataset_group_arn)\n",
    "        elif status == \"CREATE FAILED\":\n",
    "            print(\"Create failed for {}\".format(dataset_group_arn))\n",
    "            in_progress_dataset_group_arns.remove(dataset_group_arn)\n",
    "\n",
    "    if len(in_progress_dataset_group_arns) <= 0:\n",
    "        break\n",
    "    else:\n",
    "        print(\"At least one dataset group create is still in progress\")\n",
    "                \n",
    "    time.sleep(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2aa0e63",
   "metadata": {},
   "source": [
    "### Create Interactions dataset schema and datasets\n",
    "\n",
    "We will be creating a single schema for the interactions dataset type and sharing it across both solution versions. This is possible since schemas are global to your AWS account and not specific to a dataset group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ff748507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"schemaArn\": \"arn:aws:personalize:us-east-1:700974296802:schema/amazon-pantry-interactions-1\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"182216e5-c518-4963-bcc7-140284464e39\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 18:59:18 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"94\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"182216e5-c518-4963-bcc7-140284464e39\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "interactions_schema = schema = {\n",
    "    \"type\": \"record\",\n",
    "    \"name\": \"Interactions\",\n",
    "    \"namespace\": \"com.amazonaws.personalize.schema\",\n",
    "    \"fields\": [\n",
    "        {\n",
    "            \"name\": \"USER_ID\",\n",
    "            \"type\": \"string\"\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"ITEM_ID\",\n",
    "            \"type\": \"string\"\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"TIMESTAMP\",\n",
    "            \"type\": \"long\"\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"EVENT_VALUE\",\n",
    "            \"type\": \"float\"\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"EVENT_TYPE\",\n",
    "            \"type\": \"string\"\n",
    "        }\n",
    "    ],\n",
    "    \"version\": \"1.0\"\n",
    "}\n",
    "            \n",
    "create_schema_response = personalize.create_schema(\n",
    "    name = \"amazon-pantry-interactions-1\",\n",
    "    schema = json.dumps(interactions_schema)\n",
    ")\n",
    "\n",
    "interaction_schema_arn = create_schema_response['schemaArn']\n",
    "print(json.dumps(create_schema_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaaad351",
   "metadata": {},
   "source": [
    "Next we'll create an Interactions dataset in our dataset group specifying the schema we just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23e3d396",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"datasetArn\": \"arn:aws:personalize:us-east-1:700974296802:dataset/amazon-pantry-aws-similar-items/INTERACTIONS\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"29a079b8-2b5a-42e5-8fcb-b47daea8a2da\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 18:59:24 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"112\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"29a079b8-2b5a-42e5-8fcb-b47daea8a2da\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "dataset_type = \"INTERACTIONS\"\n",
    "create_dataset_response = personalize.create_dataset(\n",
    "    name = \"amazon-pantry-ints\",\n",
    "    datasetType = dataset_type,\n",
    "    datasetGroupArn = dataset_group,\n",
    "    schemaArn = interaction_schema_arn\n",
    ")\n",
    "\n",
    "interactions_dataset = create_dataset_response['datasetArn']\n",
    "print(json.dumps(create_dataset_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967d1c22",
   "metadata": {},
   "source": [
    "### Stage Interactions CSV in S3\n",
    "\n",
    "Before we can upload the interactions CSV we created earlier into the Personalize datasets that we just created, we need to stage the CSV in an S3 bucket.\n",
    "\n",
    "Let's create an S3 bucket and copy the interactions CSV file to the bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7d65344e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "us-east-1\n"
     ]
    }
   ],
   "source": [
    "# Determine the current S3 region where this notebook is being hosted in SageMaker.\n",
    "with open('/opt/ml/metadata/resource-metadata.json') as notebook_info:\n",
    "    data = json.load(notebook_info)\n",
    "    resource_arn = data['ResourceArn']\n",
    "    region = resource_arn.split(':')[3]\n",
    "print(region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8be1b7e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amazon-pantry-personalize-example-700974296802\n"
     ]
    }
   ],
   "source": [
    "s3 = boto3.client('s3')\n",
    "account_id = boto3.client('sts').get_caller_identity().get('Account')\n",
    "bucket_name = \"amazon-pantry-personalize-example-\"+account_id\n",
    "print(bucket_name)\n",
    "if region == \"us-east-1\":\n",
    "    s3.create_bucket(Bucket=bucket_name)\n",
    "else:\n",
    "    s3.create_bucket(\n",
    "        Bucket=bucket_name,\n",
    "        CreateBucketConfiguration={'LocationConstraint': region}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c681ea98",
   "metadata": {},
   "source": [
    "#### Upload Interactions CSV to S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "17bfe570",
   "metadata": {},
   "outputs": [],
   "source": [
    "boto3.Session().resource('s3').Bucket(bucket_name).Object(interactions_filename).upload_file(interactions_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf04925",
   "metadata": {},
   "source": [
    "### Create S3 bucket policy and IAM role\n",
    "\n",
    "Before we can submit a dataset import job to Personalize, we have to create a bucket policy and IAM role that will give Personalize access to our bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8383c4fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '0E27PSK0APNDR8ZS',\n",
       "  'HostId': '5+8TZhHrWI/UPqeGEIaVcZYK+hXeVSEhnT7Zfg1uzULF/+kvM7K6WUvxAio+Dlm8gSX5/kSWB1U=',\n",
       "  'HTTPStatusCode': 204,\n",
       "  'HTTPHeaders': {'x-amz-id-2': '5+8TZhHrWI/UPqeGEIaVcZYK+hXeVSEhnT7Zfg1uzULF/+kvM7K6WUvxAio+Dlm8gSX5/kSWB1U=',\n",
       "   'x-amz-request-id': '0E27PSK0APNDR8ZS',\n",
       "   'date': 'Tue, 20 Dec 2022 19:04:39 GMT',\n",
       "   'server': 'AmazonS3'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "policy = {\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Id\": \"PersonalizeS3BucketAccessPolicy\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "            \"Sid\": \"PersonalizeS3BucketAccessPolicy\",\n",
    "            \"Effect\": \"Allow\",\n",
    "            \"Principal\": {\n",
    "                \"Service\": \"personalize.amazonaws.com\"\n",
    "            },\n",
    "            \"Action\": [\n",
    "                \"s3:*Object\",\n",
    "                \"s3:ListBucket\"\n",
    "            ],\n",
    "            \"Resource\": [\n",
    "                \"arn:aws:s3:::{}\".format(bucket_name),\n",
    "                \"arn:aws:s3:::{}/*\".format(bucket_name)\n",
    "            ]\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "s3.put_bucket_policy(Bucket=bucket_name, Policy=json.dumps(policy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9267f46e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:iam::700974296802:role/PersonalizeRoleAmazonPantryAwsSimilarItems\n"
     ]
    }
   ],
   "source": [
    "iam = boto3.client(\"iam\")\n",
    "\n",
    "role_name = \"PersonalizeRoleAmazonPantryAwsSimilarItems\"\n",
    "assume_role_policy_document = {\n",
    "    \"Version\": \"2012-10-17\",\n",
    "    \"Statement\": [\n",
    "        {\n",
    "          \"Effect\": \"Allow\",\n",
    "          \"Principal\": {\n",
    "            \"Service\": \"personalize.amazonaws.com\"\n",
    "          },\n",
    "          \"Action\": \"sts:AssumeRole\"\n",
    "        }\n",
    "    ]\n",
    "}\n",
    "\n",
    "create_role_response = iam.create_role(\n",
    "    RoleName = role_name,\n",
    "    AssumeRolePolicyDocument = json.dumps(assume_role_policy_document)\n",
    ")\n",
    "\n",
    "# AmazonPersonalizeFullAccess provides access to any S3 bucket with a name that includes \"personalize\" or \"Personalize\" \n",
    "# if you would like to use a bucket with a different name, please consider creating and attaching a new policy\n",
    "# that provides read access to your bucket or attaching the AmazonS3ReadOnlyAccess policy to the role\n",
    "policy_arn = \"arn:aws:iam::aws:policy/service-role/AmazonPersonalizeFullAccess\"\n",
    "iam.attach_role_policy(\n",
    "    RoleName = role_name,\n",
    "    PolicyArn = policy_arn\n",
    ")\n",
    "\n",
    "# Now add S3 support\n",
    "iam.attach_role_policy(\n",
    "    PolicyArn='arn:aws:iam::aws:policy/AmazonS3FullAccess',\n",
    "    RoleName=role_name\n",
    ")\n",
    "time.sleep(20) # wait for a minute to allow IAM role policy attachment to propagate\n",
    "\n",
    "role_arn = create_role_response[\"Role\"][\"Arn\"]\n",
    "print(role_arn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a79e00",
   "metadata": {},
   "source": [
    "### Import Interactions datasets\n",
    "\n",
    "Now we're ready to import the staged Interactions CSV in our S3 bucket to the Personalize dataset we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "554f1095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"datasetImportJobArn\": \"arn:aws:personalize:us-east-1:700974296802:dataset-import-job/amazon-pantry-interactions-import\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"3d3b7b9b-dd81-4a59-8130-1d41d7734a1c\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:05:02 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"121\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"3d3b7b9b-dd81-4a59-8130-1d41d7734a1c\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "create_dataset_import_job_response = personalize.create_dataset_import_job(\n",
    "    jobName = \"amazon-pantry-interactions-import\",\n",
    "    datasetArn = interactions_dataset,\n",
    "    dataSource = {\n",
    "        \"dataLocation\": \"s3://{}/{}\".format(bucket_name, interactions_filename)\n",
    "    },\n",
    "    roleArn = role_arn\n",
    ")\n",
    "\n",
    "dataset_import_job = create_dataset_import_job_response['datasetImportJobArn']\n",
    "print(json.dumps(create_dataset_import_job_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a11e39a",
   "metadata": {},
   "source": [
    "### Create Items dataset schema and datasets\n",
    "\n",
    "Next we will repeat the process for the items datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef1201e",
   "metadata": {},
   "source": [
    "Create a schema that includes the description. Be sure to take note of the `\"textual\": True` attribute on the `DESCRIPTION` field. This is how you differentiate unstructured text fields from categorical and string fields. Without this attribute, Personalize will not apply natural language processing techniques to extract features from this text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3b1dc002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"schemaArn\": \"arn:aws:personalize:us-east-1:700974296802:schema/amazon-pantry-items-schema\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"20edaf31-e610-458a-aeb2-a1c0dfc77a1f\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:05:02 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"92\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"20edaf31-e610-458a-aeb2-a1c0dfc77a1f\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "item_schema = {\n",
    "    \"type\": \"record\",\n",
    "    \"name\": \"Items\",\n",
    "    \"namespace\": \"com.amazonaws.personalize.schema\",\n",
    "    \"fields\": [\n",
    "        {\n",
    "            \"name\": \"ITEM_ID\",\n",
    "            \"type\": \"string\"\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"BRAND\",\n",
    "            \"type\": [ \"null\", \"string\" ],\n",
    "            \"categorical\": True\n",
    "        },{\n",
    "            \"name\": \"PRICE\",\n",
    "            \"type\": [ \"null\", \"float\" ],\n",
    "        },{\n",
    "            \"name\": \"DESCRIPTION\",\n",
    "            \"type\": [ \"null\", \"string\" ],\n",
    "            \"textual\": True\n",
    "        }\n",
    "    ],\n",
    "    \"version\": \"1.0\"\n",
    "}\n",
    "\n",
    "create_schema_response = personalize.create_schema(\n",
    "    name = \"amazon-pantry-items-schema\",\n",
    "    schema = json.dumps(item_schema)\n",
    ")\n",
    "\n",
    "item_schema_arn = create_schema_response['schemaArn']\n",
    "print(json.dumps(create_schema_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e88ab99f",
   "metadata": {},
   "source": [
    "Next we will create Personalize datasets in our dataset group, taking special care to specify the approprate schema ARN for each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b85a1c0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"datasetArn\": \"arn:aws:personalize:us-east-1:700974296802:dataset/amazon-pantry-aws-similar-items/ITEMS\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"ae913eb6-187d-4d3b-b5ac-b2344202de2c\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:05:04 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"105\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"ae913eb6-187d-4d3b-b5ac-b2344202de2c\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "dataset_type = \"ITEMS\"\n",
    "create_dataset_response = personalize.create_dataset(\n",
    "    name = \"amazon-pantry-items\",\n",
    "    datasetType = dataset_type,\n",
    "    datasetGroupArn = dataset_group_arn,\n",
    "    schemaArn = item_schema_arn\n",
    ")\n",
    "\n",
    "items_dataset_arn = create_dataset_response['datasetArn']\n",
    "print(json.dumps(create_dataset_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043591ac",
   "metadata": {},
   "source": [
    "#### Stage Items CSV in S3\n",
    "\n",
    "Next we'll copy our two items CSV files to the same S3 bucket create above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b58d6179",
   "metadata": {},
   "outputs": [],
   "source": [
    "boto3.Session().resource('s3').Bucket(bucket_name).Object(items_filename).upload_file(items_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8074e790",
   "metadata": {},
   "source": [
    "### Import Items datasets for each dataset group\n",
    "\n",
    "Since the S3 bucket policy and IAM role are already setup, we can just submit two dataset import jobs to import the Items CSVs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a68771b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"datasetImportJobArn\": \"arn:aws:personalize:us-east-1:700974296802:dataset-import-job/amazon-pantry-items-import-job\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"5522317a-385a-45d0-a1f8-1ce8c1ec9f5d\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:05:44 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"118\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"5522317a-385a-45d0-a1f8-1ce8c1ec9f5d\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "create_dataset_import_job_response = personalize.create_dataset_import_job(\n",
    "    jobName = \"amazon-pantry-items-import-job\",\n",
    "    datasetArn = items_dataset_arn,\n",
    "    dataSource = {\n",
    "        \"dataLocation\": \"s3://{}/{}\".format(bucket_name, items_filename)\n",
    "    },\n",
    "    roleArn = role_arn\n",
    ")\n",
    "\n",
    "dataset_import_job_items_arn = create_dataset_import_job_response['datasetImportJobArn']\n",
    "print(json.dumps(create_dataset_import_job_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a58cad8",
   "metadata": {},
   "source": [
    "### Wait for Interactions dataset import jobs to complete\n",
    "\n",
    "The following cell will wait for both import jobs to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a7839815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one dataset import job is still in progress\n",
      "At least one dataset import job is still in progress\n",
      "At least one dataset import job is still in progress\n",
      "At least one dataset import job is still in progress\n",
      "Dataset import succeeded for arn:aws:personalize:us-east-1:700974296802:dataset-import-job/amazon-pantry-interactions-import\n",
      "CPU times: user 61.4 ms, sys: 1.39 ms, total: 62.8 ms\n",
      "Wall time: 4min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "in_progress_import_arns = [ dataset_import_job ]\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    for import_arn in in_progress_import_arns:\n",
    "        describe_dataset_import_job_response = personalize.describe_dataset_import_job(\n",
    "            datasetImportJobArn = import_arn\n",
    "        )\n",
    "        status = describe_dataset_import_job_response[\"datasetImportJob\"]['status']\n",
    "        if status == \"ACTIVE\":\n",
    "            print(\"Dataset import succeeded for {}\".format(import_arn))\n",
    "            in_progress_import_arns.remove(import_arn)\n",
    "        elif status == \"CREATE FAILED\":\n",
    "            print(\"Create failed for {}\".format(import_arn))\n",
    "            in_progress_import_arns.remove(import_arn)\n",
    "\n",
    "    if len(in_progress_import_arns) <= 0:\n",
    "        break\n",
    "    else:\n",
    "        print(\"At least one dataset import job is still in progress\")\n",
    "                \n",
    "    time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e594ce39",
   "metadata": {},
   "source": [
    "### Wait for Items import job to complete\n",
    "\n",
    "The following logic will wait until both items datasets are fully imported into each dataset group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "68f3c1d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one dataset import job is still in progress\n",
      "At least one dataset import job is still in progress\n",
      "Dataset import succeeded for arn:aws:personalize:us-east-1:700974296802:dataset-import-job/amazon-pantry-items-import-job\n",
      "CPU times: user 29.1 ms, sys: 4.95 ms, total: 34 ms\n",
      "Wall time: 2min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "in_progress_import_arns = [ dataset_import_job_items_arn ]\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    for import_arn in in_progress_import_arns:\n",
    "        describe_dataset_import_job_response = personalize.describe_dataset_import_job(\n",
    "            datasetImportJobArn = import_arn\n",
    "        )\n",
    "        status = describe_dataset_import_job_response[\"datasetImportJob\"]['status']\n",
    "        if status == \"ACTIVE\":\n",
    "            print(\"Dataset import succeeded for {}\".format(import_arn))\n",
    "            in_progress_import_arns.remove(import_arn)\n",
    "        elif status == \"CREATE FAILED\":\n",
    "            print(\"Create failed for {}\".format(import_arn))\n",
    "            in_progress_import_arns.remove(import_arn)\n",
    "\n",
    "    if len(in_progress_import_arns) <= 0:\n",
    "        break\n",
    "    else:\n",
    "        print(\"At least one dataset import job is still in progress\")\n",
    "                \n",
    "    time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7635527a",
   "metadata": {},
   "source": [
    "## Create solutions and solution versions\n",
    "\n",
    "With the interactions and items datasets imported into each dataset group, we will next create solutions and solution versions using the user-personalization recipe for the data in each dataset group.\n",
    "\n",
    "First, let's list the Personalize recipes available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "19f04885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'recipes': [{'name': 'aws-ecomm-customers-who-viewed-x-also-viewed',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-ecomm-customers-who-viewed-x-also-viewed',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal()),\n",
       "   'domain': 'ECOMMERCE'},\n",
       "  {'name': 'aws-ecomm-frequently-bought-together',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-ecomm-frequently-bought-together',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal()),\n",
       "   'domain': 'ECOMMERCE'},\n",
       "  {'name': 'aws-ecomm-popular-items-by-purchases',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-ecomm-popular-items-by-purchases',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal()),\n",
       "   'domain': 'ECOMMERCE'},\n",
       "  {'name': 'aws-ecomm-popular-items-by-views',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-ecomm-popular-items-by-views',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal()),\n",
       "   'domain': 'ECOMMERCE'},\n",
       "  {'name': 'aws-ecomm-recommended-for-you',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-ecomm-recommended-for-you',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal()),\n",
       "   'domain': 'ECOMMERCE'},\n",
       "  {'name': 'aws-item-affinity',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-item-affinity',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2021, 7, 15, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())},\n",
       "  {'name': 'aws-item-attribute-affinity',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-item-attribute-affinity',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2021, 8, 25, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())},\n",
       "  {'name': 'aws-personalized-ranking',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-personalized-ranking',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())},\n",
       "  {'name': 'aws-popularity-count',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-popularity-count',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())},\n",
       "  {'name': 'aws-similar-items',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-similar-items',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())},\n",
       "  {'name': 'aws-sims',\n",
       "   'recipeArn': 'arn:aws:personalize:::recipe/aws-sims',\n",
       "   'status': 'ACTIVE',\n",
       "   'creationDateTime': datetime.datetime(2019, 6, 10, 0, 0, tzinfo=tzlocal()),\n",
       "   'lastUpdatedDateTime': datetime.datetime(2022, 12, 14, 15, 7, 41, 686000, tzinfo=tzlocal())}],\n",
       " 'nextToken': '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',\n",
       " 'ResponseMetadata': {'RequestId': '2ed73980-e6ff-4913-9184-6fa2fb5c8201',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'date': 'Tue, 20 Dec 2022 19:11:50 GMT',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '3217',\n",
       "   'connection': 'keep-alive',\n",
       "   'x-amzn-requestid': '2ed73980-e6ff-4913-9184-6fa2fb5c8201'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personalize.list_recipes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebae9b1e",
   "metadata": {},
   "source": [
    "We will use the `aws-sims` and `aws-similar-items` recipes to train two solutions in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "110cb399",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims_recipe_arn = \"arn:aws:personalize:::recipe/aws-sims\"\n",
    "similar_items_recipe_arn = \"arn:aws:personalize:::recipe/aws-similar-items\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a2bda0",
   "metadata": {},
   "source": [
    "First, we will create a solution and solution version for each of the recipes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1e3564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims_create_solution_response = personalize.create_solution(\n",
    "    name = \"amazon-pantry-sims-solution-example\",\n",
    "    datasetGroupArn = dataset_group_arn,\n",
    "    recipeArn = sims_recipe_arn\n",
    ")\n",
    "\n",
    "sims_solution_arn = sims_create_solution_response['solutionArn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a3934358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:personalize:us-east-1:700974296802:solution/amazon-pantry-sims-solution-example\n"
     ]
    }
   ],
   "source": [
    "print(sims_solution_arn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "79c782fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims_solution_version_response = personalize.create_solution_version(\n",
    "    solutionArn = sims_solution_arn\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "75a996bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"solutionVersionArn\": \"arn:aws:personalize:us-east-1:700974296802:solution/amazon-pantry-sims-solution-example/55a07cc5\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"729b28f0-58dc-4252-9b80-f9fea5ee4537\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:11:50 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"121\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"729b28f0-58dc-4252-9b80-f9fea5ee4537\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "sims_solution_version_arn = sims_solution_version_response['solutionVersionArn']\n",
    "print(json.dumps(sims_solution_version_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485eb27a",
   "metadata": {},
   "source": [
    "Next we will create a solution and solution version in the dataset group that includes the item descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4db4cc06",
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_items_create_solution_response = personalize.create_solution(\n",
    "    name = \"amazon-pantry-aws-similar-items-solution-example\",\n",
    "    datasetGroupArn = dataset_group_arn,\n",
    "    recipeArn = similar_items_recipe_arn\n",
    ")\n",
    "\n",
    "similar_items_solution_arn = similar_items_create_solution_response['solutionArn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c9751ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "similar_items_solution_version_response = personalize.create_solution_version(\n",
    "    solutionArn = similar_items_solution_arn\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7be9d36b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"solutionVersionArn\": \"arn:aws:personalize:us-east-1:700974296802:solution/amazon-pantry-aws-similar-items-solution-example/c663ea46\",\n",
      "  \"ResponseMetadata\": {\n",
      "    \"RequestId\": \"6b50de7a-6e8a-47e4-95c3-59166be7edd0\",\n",
      "    \"HTTPStatusCode\": 200,\n",
      "    \"HTTPHeaders\": {\n",
      "      \"date\": \"Tue, 20 Dec 2022 19:11:51 GMT\",\n",
      "      \"content-type\": \"application/x-amz-json-1.1\",\n",
      "      \"content-length\": \"134\",\n",
      "      \"connection\": \"keep-alive\",\n",
      "      \"x-amzn-requestid\": \"6b50de7a-6e8a-47e4-95c3-59166be7edd0\"\n",
      "    },\n",
      "    \"RetryAttempts\": 0\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "similar_items_solution_version_arn = similar_items_solution_version_response['solutionVersionArn']\n",
    "print(json.dumps(similar_items_solution_version_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a03d640",
   "metadata": {},
   "source": [
    "### Wait for solution versions to become active\n",
    "\n",
    "Finally, we'll wait for the solution versions to finish being created. This step is where Personalize trains machine learning models based on the datasets and selected recipe. Personalize will also split the interactions datasets into training and evaluation portions so it can evaluate the quality of recommendations against the trained model using held out data.\n",
    "\n",
    "You will notice that the solution version in the dataset group that includes the description data will take longer to train than the one without the description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8485b171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "Build succeeded for arn:aws:personalize:us-east-1:700974296802:solution/amazon-pantry-sims-solution-example/55a07cc5\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n",
      "At least one solution build is still in progress\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "in_progress_solution_versions = [\n",
    "    sims_solution_version_arn,\n",
    "    similar_items_solution_version_arn\n",
    "]\n",
    "\n",
    "max_time = time.time() + 10*60*60 # 10 hours\n",
    "while time.time() < max_time:\n",
    "    for solution_version_arn in in_progress_solution_versions:\n",
    "        version_response = personalize.describe_solution_version(\n",
    "            solutionVersionArn = solution_version_arn\n",
    "        )\n",
    "        status = version_response[\"solutionVersion\"][\"status\"]\n",
    "        \n",
    "        if status == \"ACTIVE\":\n",
    "            print(\"Build succeeded for {}\".format(solution_version_arn))\n",
    "            in_progress_solution_versions.remove(solution_version_arn)\n",
    "        elif status == \"CREATE FAILED\":\n",
    "            print(\"Build failed for {}\".format(solution_version_arn))\n",
    "            in_progress_solution_versions.remove(solution_version_arn)\n",
    "    \n",
    "    if len(in_progress_solution_versions) <= 0:\n",
    "        break\n",
    "    else:\n",
    "        print(\"At least one solution build is still in progress\")\n",
    "        \n",
    "    time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "910a5c49",
   "metadata": {},
   "source": [
    "Generally speaking, the addition of structured and unstructured text-based metadata will increase training time. In our case, you can see above that the solution version that trained on the dataset with product descriptions took several minutes longer than the solution version trained on the dataset without production descriptions. This difference will vary based on the composition and text values for your datasets.\n",
    "\n",
    "Let's inspect the training hours for each solution version and compare them as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b426afcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training hours sims: 2.63\n",
      "Training hours similar items: 6.976\n",
      "Difference of 165.25%\n"
     ]
    }
   ],
   "source": [
    "response = personalize.describe_solution_version(solutionVersionArn = sims_solution_version_arn)\n",
    "training_hours_sims = response['solutionVersion']['trainingHours']\n",
    "\n",
    "response = personalize.describe_solution_version(solutionVersionArn = similar_items_solution_version_arn)\n",
    "training_hours_similar_items = response['solutionVersion']['trainingHours']\n",
    "training_diff = (training_hours_similar_items - training_hours_sims) / training_hours_sims\n",
    "\n",
    "print(f\"Training hours sims: {training_hours_sims}\")\n",
    "print(f\"Training hours similar items: {training_hours_similar_items}\")\n",
    "\n",
    "print(\"Difference of {:.2%}\".format(training_diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b84c75d6",
   "metadata": {},
   "source": [
    "The training hours used for cost calculations was about 50% higher for training with the description column. \n",
    "\n",
    "The wall/clock time and training hours will vary depending on the size of your datasets but this information can help you assess the trade off when considering adding unstructured text to your datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a0a933a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sims_solution = {\n",
    "        \"solution_arn\": sims_solution_arn,\n",
    "        \"solution_version_arn\": sims_solution_version_arn\n",
    "}\n",
    "similar_items_solution = {\n",
    "        \"solution_arn\": similar_items_solution_arn,\n",
    "        \"solution_version_arn\": similar_items_solution_version_arn\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f9cacb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_campaign(solution,name):\n",
    "    create_campaign_response = personalize.create_campaign(\n",
    "        name = \"personalize-demo-\" + name + \"example\",\n",
    "        solutionVersionArn = solution['solution_version_arn'],\n",
    "        minProvisionedTPS = 1\n",
    "    )\n",
    "\n",
    "    campaign_arn = create_campaign_response['campaignArn']\n",
    "    print('campaign_arn:' + campaign_arn)\n",
    "    return campaign_arn\n",
    "\n",
    "def waitForCampaign(solution):\n",
    "    max_time = time.time() + 3*60*60 # 3 hours\n",
    "    while time.time() < max_time:\n",
    "        describe_campaign_response = personalize.describe_campaign(\n",
    "            campaignArn = solution['campaign_arn']\n",
    "        )\n",
    "        status = describe_campaign_response[\"campaign\"][\"status\"]\n",
    "        print(\"Campaign: {} {}\".format(solution['campaign_arn'], status))\n",
    "\n",
    "        if status == \"ACTIVE\" or status == \"CREATE FAILED\":\n",
    "            break\n",
    "\n",
    "        time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b8da73",
   "metadata": {},
   "source": [
    "#### Create and wait for the 2 Campaigns\n",
    "Create a campaign for each of the items similarities recipes, but keep all the other settings the same to demonstrate the impact of the addition of metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b91299a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "campaign_arn:arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample\n",
      "campaign_arn:arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-aws-similar-itemsexample\n"
     ]
    }
   ],
   "source": [
    "sims_solution['campaign_arn'] = create_campaign(sims_solution, 'sims')\n",
    "similar_items_solution['campaign_arn'] = create_campaign(similar_items_solution, 'aws-similar-items')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "775cf9c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE PENDING\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample CREATE IN_PROGRESS\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-simsexample ACTIVE\n",
      "Campaign: arn:aws:personalize:us-east-1:700974296802:campaign/personalize-demo-aws-similar-itemsexample ACTIVE\n"
     ]
    }
   ],
   "source": [
    "waitForCampaign(sims_solution)\n",
    "waitForCampaign(similar_items_solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a13ce2",
   "metadata": {},
   "source": [
    "# Getting Recommendations\n",
    "\n",
    "First we are going to select three types of items to infer with\n",
    "1. Item with a high number of interactions\n",
    "1. Item with a low number of interactions\n",
    "1. Random itemId\n",
    "\n",
    "We will then look at how each of the models behave with each of the provided items\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae13686",
   "metadata": {},
   "source": [
    "Lets take a look at our interactions dataset and plot the distribution of items that have been interacted with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "10166131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asin</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00XA9DADC</td>\n",
       "      <td>5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00I3MVY9I</td>\n",
       "      <td>5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00W6T13OU</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0014CZUZ8</td>\n",
       "      <td>3225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0014CSIG6</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10579</th>\n",
       "      <td>B011OAC4O4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10580</th>\n",
       "      <td>B011R60JDS</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10581</th>\n",
       "      <td>B019DKR9CO</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10582</th>\n",
       "      <td>B00HZYETKG</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10583</th>\n",
       "      <td>B013AV2AUI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10584 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             asin  count\n",
       "0      B00XA9DADC   5288\n",
       "1      B00I3MVY9I   5086\n",
       "2      B00W6T13OU   4023\n",
       "3      B0014CZUZ8   3225\n",
       "4      B0014CSIG6   2600\n",
       "...           ...    ...\n",
       "10579  B011OAC4O4      1\n",
       "10580  B011R60JDS      1\n",
       "10581  B019DKR9CO      1\n",
       "10582  B00HZYETKG      1\n",
       "10583  B013AV2AUI      1\n",
       "\n",
       "[10584 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_interacted_df = positive_reviews_df.copy()\n",
    "# Getting unique aisns counts\n",
    "asin_interaction_count = items_interacted_df['ITEM_ID'].value_counts()\n",
    "# Transforming panda series to df\n",
    "asin_interaction_count_df = pd.DataFrame({'asin':asin_interaction_count.index, 'count':asin_interaction_count.values})\n",
    "asin_interaction_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ddf879c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10584.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.630008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>123.480233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5288.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count\n",
       "count  10584.000000\n",
       "mean      36.630008\n",
       "std      123.480233\n",
       "min        1.000000\n",
       "25%        4.000000\n",
       "50%       12.000000\n",
       "75%       33.250000\n",
       "max     5288.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asin_interaction_count_df.describe(include='int')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adba267a",
   "metadata": {},
   "source": [
    "As we can see above, the highest interacted item has ~5k interactions, lets plot the distribution of all items. Here we can see that there are items with high number of interactions and some with very low to non interactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6bc8469c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "asin_interaction_count_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01ccd0c",
   "metadata": {},
   "source": [
    "Below we can see a closer look at items with 100 to 300 interactions. These are going to give us the most variable results when testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "986da950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom_interactions = asin_interaction_count_df.copy()\n",
    "zoom_interactions = zoom_interactions.loc[(zoom_interactions[\"count\"] > 100) & (zoom_interactions[\"count\"] < 300)]\n",
    "zoom_interactions.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c13ae0",
   "metadata": {},
   "source": [
    "Now lets define some functions to explore the items metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7de35d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.geeksforgeeks.org/how-to-select-rows-from-a-dataframe-based-on-column-values/\n",
    "def get_item_brand(item_id):\n",
    "    \"\"\"\n",
    "    Takes in an ID, returns its brand\n",
    "    \"\"\"\n",
    "\n",
    "    return items_df.query('ITEM_ID==\"{}\"'.format(item_id))['BRAND'].item()\n",
    "\n",
    "def get_item_price(item_id):\n",
    "    \"\"\"\n",
    "    Takes in an ID, returns its brand\n",
    "    \"\"\"\n",
    "\n",
    "    return items_df.query('ITEM_ID==\"{}\"'.format(item_id))['PRICE'].item()\n",
    "\n",
    "def get_item_description(item_id):\n",
    "    \"\"\"\n",
    "    Takes in an ID, returns its brand\n",
    "    \"\"\"\n",
    "\n",
    "    return items_df.query('ITEM_ID==\"{}\"'.format(item_id))['DESCRIPTION'].item()\n",
    "def get_item_df(item_id):\n",
    "    \"\"\"\n",
    "    Takes in an ID, returns a title\n",
    "    \"\"\"\n",
    "    temp_df = items_df.query('ITEM_ID==\"{}\"'.format(item_id))\n",
    "  \n",
    "    temp_df['INTERACTIONS_COUNT'] = get_item_count(item_id)\n",
    "    return temp_df\n",
    "def get_item_count(item_id):\n",
    "    return asin_interaction_count_df.query('asin==\"{}\"'.format(item_id))['count'].item()\n",
    "    \n",
    "def get_recs_df(item_id, campaign):\n",
    "    response = personalize_runtime.get_recommendations(\n",
    "        campaignArn=campaign,\n",
    "        itemId=item_id,\n",
    "        numResults=10\n",
    "    )\n",
    "    return clean_recs_list(response['itemList'])\n",
    "\n",
    "def clean_recs_list(rec_list):\n",
    "    items = []\n",
    "    for each in rec_list:\n",
    "        items.append([each['itemId'], get_item_brand(each['itemId']), get_item_price(each['itemId']), get_item_description(each['itemId']), get_item_count(each['itemId'])])\n",
    "    return pd.DataFrame (items, columns = ['ITEM_ID', 'BRAND', 'PRICE','DESCRIPTION', 'INTERACTIONS_COUNT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eebb0b3",
   "metadata": {},
   "source": [
    "Lets get a random item id and explore each model recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "67aff735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B00NPNQBOS'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asin_interaction_count_df.sample()['asin'].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e3d55d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21740/3349061916.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df['INTERACTIONS_COUNT'] = get_item_count(item_id)\n"
     ]
    }
   ],
   "source": [
    "asin = asin_interaction_count_df.sample(1)['asin'].item()\n",
    "recommended_item_df = get_item_df(asin)\n",
    "recommendations_sims_df = get_recs_df(asin,sims_solution['campaign_arn'])\n",
    "recommendations_similar_items_df = get_recs_df(asin,similar_items_solution['campaign_arn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927a61e2",
   "metadata": {},
   "source": [
    "This is the item we are going to run inference against, as we can see here we have the brand, price, and number of interactions. The theory is that for items with low interactions SIMS will return mostly popular items, and the new aws-item-similarity recipe will return items more related to the current item metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "55270a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10634</th>\n",
       "      <td>B01H37PU4S</td>\n",
       "      <td>Vermont Smoke and Cure</td>\n",
       "      <td>4.62</td>\n",
       "      <td>Real Italian sausage with an enjoyable kick of...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ITEM_ID                   BRAND  PRICE  \\\n",
       "10634  B01H37PU4S  Vermont Smoke and Cure   4.62   \n",
       "\n",
       "                                             DESCRIPTION  INTERACTIONS_COUNT  \n",
       "10634  Real Italian sausage with an enjoyable kick of...                   7  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_item_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "16c90069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Real Italian sausage with an enjoyable kick of spice. Crafted with lean pork, fennel seeds, red pepper flakes, and garlic for nourishing real meat protein that you can now enjoy on the go.'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_item_df['DESCRIPTION'].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7ecb0841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00XA9DADC</td>\n",
       "      <td>Twinings</td>\n",
       "      <td>8.00</td>\n",
       "      <td>Made with 100% pure peppermint leaves. An upli...</td>\n",
       "      <td>5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00I3MVY9I</td>\n",
       "      <td>Crest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unleash A Brilliantly Dimensional Smile A 3D W...</td>\n",
       "      <td>5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00W6T13OU</td>\n",
       "      <td>Brooklyn Beans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In Brooklyn, we know a thing or two about bein...</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00HZ6X8QU</td>\n",
       "      <td>Tide</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Tide Original liquid laundry detergent provide...</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0014CZUZ8</td>\n",
       "      <td>Planters</td>\n",
       "      <td>5.98</td>\n",
       "      <td>Nuts are the heroes of wholesome snacking. And...</td>\n",
       "      <td>3225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B00I9KR31C</td>\n",
       "      <td>Viva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>View larger Product Description Dinner is over...</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00W6T208I</td>\n",
       "      <td>Celestial Seasonings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A full-bodied black tea made from ingredients ...</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B00I8YJZ4M</td>\n",
       "      <td>Tom's of Maine</td>\n",
       "      <td>3.79</td>\n",
       "      <td>Toms of Maine Goodness Day Mt. Agamenticus loc...</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00ICZZCEE</td>\n",
       "      <td>Pure Protein</td>\n",
       "      <td>7.09</td>\n",
       "      <td>Pure Protein 23 Grams, Vanilla Creme(4 Count, ...</td>\n",
       "      <td>1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0014CSIG6</td>\n",
       "      <td>Mauna Loa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2g Net carbs. Taste of the tropics. Share this...</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID                 BRAND  PRICE  \\\n",
       "0  B00XA9DADC              Twinings   8.00   \n",
       "1  B00I3MVY9I                 Crest    NaN   \n",
       "2  B00W6T13OU        Brooklyn Beans    NaN   \n",
       "3  B00HZ6X8QU                  Tide  11.99   \n",
       "4  B0014CZUZ8              Planters   5.98   \n",
       "5  B00I9KR31C                  Viva    NaN   \n",
       "6  B00W6T208I  Celestial Seasonings    NaN   \n",
       "7  B00I8YJZ4M        Tom's of Maine   3.79   \n",
       "8  B00ICZZCEE          Pure Protein   7.09   \n",
       "9  B0014CSIG6             Mauna Loa    NaN   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Made with 100% pure peppermint leaves. An upli...                5288  \n",
       "1  Unleash A Brilliantly Dimensional Smile A 3D W...                5086  \n",
       "2  In Brooklyn, we know a thing or two about bein...                4023  \n",
       "3  Tide Original liquid laundry detergent provide...                1791  \n",
       "4  Nuts are the heroes of wholesome snacking. And...                3225  \n",
       "5  View larger Product Description Dinner is over...                1467  \n",
       "6  A full-bodied black tea made from ingredients ...                1415  \n",
       "7  Toms of Maine Goodness Day Mt. Agamenticus loc...                1403  \n",
       "8  Pure Protein 23 Grams, Vanilla Creme(4 Count, ...                1429  \n",
       "9  2g Net carbs. Taste of the tropics. Share this...                2600  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_sims_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf56e749",
   "metadata": {},
   "source": [
    "Cost category and product groupings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4752c39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B01H37PJSU</td>\n",
       "      <td>Vermont Smoke and Cure</td>\n",
       "      <td>4.99</td>\n",
       "      <td>Deep, medium spicy taste of true BBQ. Crafted ...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00I669GHS</td>\n",
       "      <td>CLIF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clif Builder's, Chocolate Peanut Butter, 6 Count</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B01H362CRC</td>\n",
       "      <td>Kleenex</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kleenex Cotton Soft Pads are quilted, cotton s...</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00099XPD6</td>\n",
       "      <td>Carnation</td>\n",
       "      <td>1.19</td>\n",
       "      <td>Carnation Evaporated Milk is fresh, nutritious...</td>\n",
       "      <td>1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B00MHTGHE0</td>\n",
       "      <td>Wonderful Pistachios &amp; Almonds</td>\n",
       "      <td>7.98</td>\n",
       "      <td>16 Ounces of our Roasted &amp; Salted Wonderful Pi...</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B00J2AV5MG</td>\n",
       "      <td>Nature Valley</td>\n",
       "      <td>4.93</td>\n",
       "      <td>Whether you're on the slopes or on the links, ...</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00OKB7W6A</td>\n",
       "      <td>Chicken of the Sea</td>\n",
       "      <td>5.48</td>\n",
       "      <td>CHICKEN OF THE SEA's Canned, pouched and froze...</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B00HZ6X8QU</td>\n",
       "      <td>Tide</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Tide Original liquid laundry detergent provide...</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00R1GZONE</td>\n",
       "      <td>Chex</td>\n",
       "      <td>2.87</td>\n",
       "      <td>At General Mills, weve been making food people...</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B00R1H0AYQ</td>\n",
       "      <td>Nature Valley</td>\n",
       "      <td>4.93</td>\n",
       "      <td>Made with 100% Natural Whole Grain Oats; 16g o...</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID                           BRAND  PRICE  \\\n",
       "0  B01H37PJSU          Vermont Smoke and Cure   4.99   \n",
       "1  B00I669GHS                            CLIF    NaN   \n",
       "2  B01H362CRC                         Kleenex    NaN   \n",
       "3  B00099XPD6                       Carnation   1.19   \n",
       "4  B00MHTGHE0  Wonderful Pistachios & Almonds   7.98   \n",
       "5  B00J2AV5MG                   Nature Valley   4.93   \n",
       "6  B00OKB7W6A              Chicken of the Sea   5.48   \n",
       "7  B00HZ6X8QU                            Tide  11.99   \n",
       "8  B00R1GZONE                            Chex   2.87   \n",
       "9  B00R1H0AYQ                   Nature Valley   4.93   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Deep, medium spicy taste of true BBQ. Crafted ...                  14  \n",
       "1   Clif Builder's, Chocolate Peanut Butter, 6 Count                 432  \n",
       "2  Kleenex Cotton Soft Pads are quilted, cotton s...                  37  \n",
       "3  Carnation Evaporated Milk is fresh, nutritious...                1291  \n",
       "4  16 Ounces of our Roasted & Salted Wonderful Pi...                 417  \n",
       "5  Whether you're on the slopes or on the links, ...                 277  \n",
       "6  CHICKEN OF THE SEA's Canned, pouched and froze...                 254  \n",
       "7  Tide Original liquid laundry detergent provide...                1791  \n",
       "8  At General Mills, weve been making food people...                 142  \n",
       "9  Made with 100% Natural Whole Grain Oats; 16g o...                 322  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_similar_items_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7235bb42",
   "metadata": {},
   "source": [
    "Let's print the descriptions too see if they match our item theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f154010d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----ITEM----\n",
      "Recommendation number 1, BRAND: Vermont Smoke and Cure, PRICE: 4.99\n",
      "----DESCRIPTION----\n",
      "Deep, medium spicy taste of true BBQ. Crafted with lean beef, peppers and molasses for nourishing real meat protein that you can now enjoy on the go.\n",
      "----ITEM----\n",
      "Recommendation number 2, BRAND: CLIF, PRICE: nan\n",
      "----DESCRIPTION----\n",
      "Clif Builder's, Chocolate Peanut Butter, 6 Count\n",
      "----ITEM----\n",
      "Recommendation number 3, BRAND: Kleenex, PRICE: nan\n",
      "----DESCRIPTION----\n",
      "Kleenex Cotton Soft Pads are quilted, cotton soft squares uniquely designed to lift makeup & impurities for an effortless facial experience. Pads are textured for an effective facial clean and durable for wet or dry use. Large and thick enough to use on both sides, yet gentle enough to work with your favorite liquid makeup remover or facial toner. The plush texture of the cleansing pads are more effective for facial cleansing, while the lint-free microfiber is perfect as a nail polish remover or nail wipe. Dermatologist tested, hypoallergenic, fragrance-free, alcohol-free, & free of inks & dyes. Cruelty-free and not tested on animals. View larger View larger View larger Versatile for Your Skincare Routine Needs. Cloths offer more effective cleansing. The multi-purpose design retains its shape for wet and dry use. View larger View larger View larger View larger\n",
      "----ITEM----\n",
      "Recommendation number 4, BRAND: Carnation, PRICE: 1.19\n",
      "----DESCRIPTION----\n",
      "Carnation Evaporated Milk is fresh, nutritious milk from which half the water has been removed.\n",
      "----ITEM----\n",
      "Recommendation number 5, BRAND: Wonderful Pistachios & Almonds, PRICE: 7.98\n",
      "----DESCRIPTION----\n",
      "16 Ounces of our Roasted & Salted Wonderful Pistachios. Fuel your fitness with The Fit Nut. Known for protein and fiber, Wonderful Pistachios are a smart snack. They’re perfect before or after a workout. So push your health to new heights with this tasty little powerhouse of heart-healthy goodness. Wonderful Pistachios are a smart, healthy choice for folks around the world. Located in Californias fertile San Joaquin Valley, Wonderful Pistachios & Almonds owns, cultivates and harvests more than 75,000 acres of pistachio and almond orchards, and delivers pounds of nuts globally each year. The warm days and cool nights work in harmony with the regions natural soils to create the perfect growing climate. We then carefully tend and harvest each pistachio using the latest in sustainable practices. Pistachios offer nutrients and minerals great for overall health. Not only are they a good source of fiber, 90% of the fat found in pistachios is unsaturated. Theres no reason to give up snacking to stay in shape. Pistachios are a good source of plant-based protein. And in addition to tasting great, a single serving includes about 49 pistachios, enough to satisfy your snacking needs.\n",
      "----ITEM----\n",
      "Recommendation number 6, BRAND: Nature Valley, PRICE: 4.93\n",
      "----DESCRIPTION----\n",
      "Whether you're on the slopes or on the links, the classic crunch of Nature Valley crunch granola bars is a perfect way to fuel up naturally. Nature Valley crunchy granola bars are 100% natural and an excellent source of whole grain. Delicious, wholesome and easy to bring along, they're the energy bar nature intended.\n",
      "----ITEM----\n",
      "Recommendation number 7, BRAND: Chicken of the Sea, PRICE: 5.48\n",
      "----DESCRIPTION----\n",
      "CHICKEN OF THE SEA's Canned, pouched and frozen seafood is an almost perfect food! It's an inexpensive and easy-to-prepare source of important nutrients like omega-3, calcium, protein and more. In addition, Chicken of the Sea's canned and pouched tuna and salmon, frozen shrimp and other seafood are low in fat and calories when compared with other proteins.\n",
      "----ITEM----\n",
      "Recommendation number 8, BRAND: Tide, PRICE: 11.99\n",
      "----DESCRIPTION----\n",
      "Tide Original liquid laundry detergent provides the Tide clean you love, now more concentrated for more stain removal and freshness and less water*. Americas #1 detergent** is specifically designed to cover your many laundry needs. Also try our small but powerful Tide PODS laundry pacs. Measure your loads with cap. For medium loads, fill to bar 1. For large loads, fill to bar 3. For HE full loads, fill to bar 5. Add clothes, pour into dispenser, start washer. * vs. previous formula ** based on sales For an amazing clean you know and love, theres only one original: Tide Original Scent High Efficiency Laundry Detergent which removes over 100 stains in one wash. Its revitalizing scent is infused with aloe and floral notes to help keep your familys clothes smelling as great as they look. The formula also allows you to enjoy Tide even more as it lasts up to 2x longer compared to leading baking soda detergent based on large loads use instructions (175 oz and smaller bottles). View larger View larger View larger View larger\n",
      "----ITEM----\n",
      "Recommendation number 9, BRAND: Chex, PRICE: 2.87\n",
      "----DESCRIPTION----\n",
      "At General Mills, weve been making food people love for more than 150 years, including some of your family's favorite cereals. Whether you love Honey Nut Cheerios or Wheaties, Lucky Charms or Cinnamon Toast Crunch, you can feel good about loving cereal. All Big G cereals have at least 10 grams of whole grain per serving, and are fortified with vitamins and minerals. In addition, 75% of our cereals today are made without artificial flavors or colors from artificial sources, and have been that way for a long time. We're also working to remove those ingredients from the rest of our cereals.\n",
      "----ITEM----\n",
      "Recommendation number 10, BRAND: Nature Valley, PRICE: 4.93\n",
      "----DESCRIPTION----\n",
      "Made with 100% Natural Whole Grain Oats; 16g of Whole Grain per Serving - 48g Whole Grain recommended daily Whole grain oats are the first ingredient in Nature Valley Crunchy Bars. Made with simple ingredients for a pure, simple taste. Nature Valley Crunchy Bars provide the energy you need to conquer everyday challenges.\n"
     ]
    }
   ],
   "source": [
    "for index, row in recommendations_similar_items_df.iterrows():\n",
    "    print('----ITEM----')\n",
    "    print('Recommendation number {index}, BRAND: {brand}, PRICE: {price}'.format(index=index+1, brand=row['BRAND'], price=row['PRICE']))\n",
    "    print('----DESCRIPTION----')\n",
    "    print(row['DESCRIPTION'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaa42b0",
   "metadata": {},
   "source": [
    "# Targeted examples\n",
    "\n",
    "Now let's take a look at some more targeted items to see how these two models behave"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b110ccc",
   "metadata": {},
   "source": [
    "## Popular item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fb22c722",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21740/3349061916.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df['INTERACTIONS_COUNT'] = get_item_count(item_id)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>B00HZ6X8QU</td>\n",
       "      <td>Tide</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Tide Original liquid laundry detergent provide...</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ITEM_ID BRAND  PRICE  \\\n",
       "709  B00HZ6X8QU  Tide  11.99   \n",
       "\n",
       "                                           DESCRIPTION  INTERACTIONS_COUNT  \n",
       "709  Tide Original liquid laundry detergent provide...                1791  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asin = 'B00HZ6X8QU'\n",
    "recommended_item_df = get_item_df(asin)\n",
    "recommendations_sims_df = get_recs_df(asin,sims_solution['campaign_arn'])\n",
    "recommendations_similar_items_df = get_recs_df(asin,similar_items_solution['campaign_arn'])\n",
    "recommended_item_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec859ebc",
   "metadata": {},
   "source": [
    "The theory is that for items with low interactions SIMS will return mostly popular items, and the new aws-item-similarity recipe will return items more related to the current item metadata (price and description)\n",
    "\n",
    "**Full description:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8e8fc176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tide Original liquid laundry detergent provides the Tide clean you love, now more concentrated for more stain removal and freshness and less water*. Americas #1 detergent** is specifically designed to cover your many laundry needs. Also try our small but powerful Tide PODS laundry pacs. Measure your loads with cap. For medium loads, fill to bar 1. For large loads, fill to bar 3. For HE full loads, fill to bar 5. Add clothes, pour into dispenser, start washer. * vs. previous formula ** based on sales For an amazing clean you know and love, theres only one original: Tide Original Scent High Efficiency Laundry Detergent which removes over 100 stains in one wash. Its revitalizing scent is infused with aloe and floral notes to help keep your familys clothes smelling as great as they look. The formula also allows you to enjoy Tide even more as it lasts up to 2x longer compared to leading baking soda detergent based on large loads use instructions (175 oz and smaller bottles). View larger View larger View larger View larger'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_item_df['DESCRIPTION'].item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fe2b4c",
   "metadata": {},
   "source": [
    "#### SIMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b7076b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00JGZBTCI</td>\n",
       "      <td>Downy</td>\n",
       "      <td>8.47</td>\n",
       "      <td>Downy April Fresh Liquid Fabric Conditioner so...</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00MTSGYG0</td>\n",
       "      <td>Bounce</td>\n",
       "      <td>8.24</td>\n",
       "      <td>We admit, the outdoors smell great. But with o...</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00JGZCLIY</td>\n",
       "      <td>Downy</td>\n",
       "      <td>7.94</td>\n",
       "      <td>No dyes, no perfumes, no worries: Downy Fabric...</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00PFPDRRI</td>\n",
       "      <td>Dawn</td>\n",
       "      <td>1.92</td>\n",
       "      <td>Dawn Non-Concentrated dishwashing liquid soap ...</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B00UBJQHMU</td>\n",
       "      <td>Listerine</td>\n",
       "      <td>5.99</td>\n",
       "      <td>Protect your mouth from germs and get fresh br...</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B01B4N9Y6C</td>\n",
       "      <td>Cottonelle</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Cottonelle Clean Care toilet paper with Clean ...</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00I9JK8GU</td>\n",
       "      <td>Ziploc</td>\n",
       "      <td>4.75</td>\n",
       "      <td>Get more out of your everyday with Ziplocbrand...</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B00UB7ASK4</td>\n",
       "      <td>Old Spice</td>\n",
       "      <td>7.47</td>\n",
       "      <td>Old Spice Men's Deodorant reduces underarm odo...</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00JGZBUPE</td>\n",
       "      <td>Downy</td>\n",
       "      <td>5.11</td>\n",
       "      <td>Downy April Fresh Dryer Sheets fight static cl...</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B00IA6ILKC</td>\n",
       "      <td>Glad</td>\n",
       "      <td>8.49</td>\n",
       "      <td>Glad OdorShield Trash Bags in Fresh Clean Scen...</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID       BRAND  PRICE  \\\n",
       "0  B00JGZBTCI       Downy   8.47   \n",
       "1  B00MTSGYG0      Bounce   8.24   \n",
       "2  B00JGZCLIY       Downy   7.94   \n",
       "3  B00PFPDRRI        Dawn   1.92   \n",
       "4  B00UBJQHMU   Listerine   5.99   \n",
       "5  B01B4N9Y6C  Cottonelle   6.99   \n",
       "6  B00I9JK8GU      Ziploc   4.75   \n",
       "7  B00UB7ASK4   Old Spice   7.47   \n",
       "8  B00JGZBUPE       Downy   5.11   \n",
       "9  B00IA6ILKC        Glad   8.49   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Downy April Fresh Liquid Fabric Conditioner so...                 544  \n",
       "1  We admit, the outdoors smell great. But with o...                 394  \n",
       "2  No dyes, no perfumes, no worries: Downy Fabric...                 278  \n",
       "3  Dawn Non-Concentrated dishwashing liquid soap ...                 182  \n",
       "4  Protect your mouth from germs and get fresh br...                 483  \n",
       "5  Cottonelle Clean Care toilet paper with Clean ...                 570  \n",
       "6  Get more out of your everyday with Ziplocbrand...                 475  \n",
       "7  Old Spice Men's Deodorant reduces underarm odo...                  49  \n",
       "8  Downy April Fresh Dryer Sheets fight static cl...                 117  \n",
       "9  Glad OdorShield Trash Bags in Fresh Clean Scen...                 380  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_sims_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a73599",
   "metadata": {},
   "source": [
    "We can see here, the SIMS model returns popular items. Which is not optimal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c07ccdd",
   "metadata": {},
   "source": [
    "#### Similar Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5b02f6b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00JGZBTCI</td>\n",
       "      <td>Downy</td>\n",
       "      <td>8.47</td>\n",
       "      <td>Downy April Fresh Liquid Fabric Conditioner so...</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00MTSGYG0</td>\n",
       "      <td>Bounce</td>\n",
       "      <td>8.24</td>\n",
       "      <td>We admit, the outdoors smell great. But with o...</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00UBJQHMU</td>\n",
       "      <td>Listerine</td>\n",
       "      <td>5.99</td>\n",
       "      <td>Protect your mouth from germs and get fresh br...</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B011DX5G92</td>\n",
       "      <td>Charmin</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Charmin Ultra Strong toilet paper cleans bette...</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B019XEY5GS</td>\n",
       "      <td>Bounty</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Ever try one of those bargain' paper towels an...</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B00HZYDW5E</td>\n",
       "      <td>Coca-Cola</td>\n",
       "      <td>4.68</td>\n",
       "      <td></td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00PC5CI8K</td>\n",
       "      <td>Rice Krispies Treats</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Make snack time a little sweeter with Snap, Cr...</td>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B015TYDZTE</td>\n",
       "      <td>Bounty</td>\n",
       "      <td>11.42</td>\n",
       "      <td>Ever try one of those bargain' paper towels an...</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00JGZCLIY</td>\n",
       "      <td>Downy</td>\n",
       "      <td>7.94</td>\n",
       "      <td>No dyes, no perfumes, no worries: Downy Fabric...</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0010XS2CE</td>\n",
       "      <td>Softsoap</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Experience the soothing fragrance of lavender ...</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID                 BRAND  PRICE  \\\n",
       "0  B00JGZBTCI                 Downy   8.47   \n",
       "1  B00MTSGYG0                Bounce   8.24   \n",
       "2  B00UBJQHMU             Listerine   5.99   \n",
       "3  B011DX5G92               Charmin   6.99   \n",
       "4  B019XEY5GS                Bounty  14.99   \n",
       "5  B00HZYDW5E             Coca-Cola   4.68   \n",
       "6  B00PC5CI8K  Rice Krispies Treats   3.68   \n",
       "7  B015TYDZTE                Bounty  11.42   \n",
       "8  B00JGZCLIY                 Downy   7.94   \n",
       "9  B0010XS2CE              Softsoap   2.99   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Downy April Fresh Liquid Fabric Conditioner so...                 544  \n",
       "1  We admit, the outdoors smell great. But with o...                 394  \n",
       "2  Protect your mouth from germs and get fresh br...                 483  \n",
       "3  Charmin Ultra Strong toilet paper cleans bette...                 536  \n",
       "4  Ever try one of those bargain' paper towels an...                 483  \n",
       "5                                                                    408  \n",
       "6  Make snack time a little sweeter with Snap, Cr...                 737  \n",
       "7  Ever try one of those bargain' paper towels an...                 606  \n",
       "8  No dyes, no perfumes, no worries: Downy Fabric...                 278  \n",
       "9  Experience the soothing fragrance of lavender ...                 818  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_similar_items_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a0a01e",
   "metadata": {},
   "source": [
    "We see very comparable recommendations across both models! Which is an expected behavior considering the item is one of the most popular across the interactions dataset. Now less take a look at recommendations of an item not as popular"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2290c41a",
   "metadata": {},
   "source": [
    "## Unpopular item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2f751d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_21740/3349061916.py:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp_df['INTERACTIONS_COUNT'] = get_item_count(item_id)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>B01GCT22E4</td>\n",
       "      <td>Pepsi</td>\n",
       "      <td>3.07</td>\n",
       "      <td>With its light, crisp taste, Diet Pepsi gives ...</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ITEM_ID  BRAND  PRICE  \\\n",
       "10456  B01GCT22E4  Pepsi   3.07   \n",
       "\n",
       "                                             DESCRIPTION  INTERACTIONS_COUNT  \n",
       "10456  With its light, crisp taste, Diet Pepsi gives ...                   6  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B017BGLXYC - SODA - low price - low interactions\n",
    "asin = 'B01GCT22E4'\n",
    "recommended_item_df = get_item_df(asin)\n",
    "recommendations_sims_df = get_recs_df(asin,sims_solution['campaign_arn'])\n",
    "recommendations_sims_v2_df = get_recs_df(asin,similar_items_solution['campaign_arn'])\n",
    "recommended_item_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5586dd46",
   "metadata": {},
   "source": [
    "The theory is that for items with low interactions SIMS will return mostly popular items, and the new aws-item-similarity recipe will return items more related to the current item metadata (price and description)\n",
    "\n",
    "**Full description:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c8b72985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'With its light, crisp taste, Diet Pepsi gives you all the refreshment you need - with zero sugar, zero calories and zero carbs. Light. Crisp. Refreshing. Diet Pepsi. Nothing refreshes like a Diet Pepsi'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_item_df['DESCRIPTION'].item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a629f08",
   "metadata": {},
   "source": [
    "#### SIMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "70568b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00XA9DADC</td>\n",
       "      <td>Twinings</td>\n",
       "      <td>8.00</td>\n",
       "      <td>Made with 100% pure peppermint leaves. An upli...</td>\n",
       "      <td>5288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00I3MVY9I</td>\n",
       "      <td>Crest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unleash A Brilliantly Dimensional Smile A 3D W...</td>\n",
       "      <td>5086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00W6T13OU</td>\n",
       "      <td>Brooklyn Beans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>In Brooklyn, we know a thing or two about bein...</td>\n",
       "      <td>4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00HZ6X8QU</td>\n",
       "      <td>Tide</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Tide Original liquid laundry detergent provide...</td>\n",
       "      <td>1791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0014CZUZ8</td>\n",
       "      <td>Planters</td>\n",
       "      <td>5.98</td>\n",
       "      <td>Nuts are the heroes of wholesome snacking. And...</td>\n",
       "      <td>3225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B00I9KR31C</td>\n",
       "      <td>Viva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>View larger Product Description Dinner is over...</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00W6T208I</td>\n",
       "      <td>Celestial Seasonings</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A full-bodied black tea made from ingredients ...</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B00I8YJZ4M</td>\n",
       "      <td>Tom's of Maine</td>\n",
       "      <td>3.79</td>\n",
       "      <td>Toms of Maine Goodness Day Mt. Agamenticus loc...</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00ICZZCEE</td>\n",
       "      <td>Pure Protein</td>\n",
       "      <td>7.09</td>\n",
       "      <td>Pure Protein 23 Grams, Vanilla Creme(4 Count, ...</td>\n",
       "      <td>1429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0014CSIG6</td>\n",
       "      <td>Mauna Loa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2g Net carbs. Taste of the tropics. Share this...</td>\n",
       "      <td>2600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID                 BRAND  PRICE  \\\n",
       "0  B00XA9DADC              Twinings   8.00   \n",
       "1  B00I3MVY9I                 Crest    NaN   \n",
       "2  B00W6T13OU        Brooklyn Beans    NaN   \n",
       "3  B00HZ6X8QU                  Tide  11.99   \n",
       "4  B0014CZUZ8              Planters   5.98   \n",
       "5  B00I9KR31C                  Viva    NaN   \n",
       "6  B00W6T208I  Celestial Seasonings    NaN   \n",
       "7  B00I8YJZ4M        Tom's of Maine   3.79   \n",
       "8  B00ICZZCEE          Pure Protein   7.09   \n",
       "9  B0014CSIG6             Mauna Loa    NaN   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Made with 100% pure peppermint leaves. An upli...                5288  \n",
       "1  Unleash A Brilliantly Dimensional Smile A 3D W...                5086  \n",
       "2  In Brooklyn, we know a thing or two about bein...                4023  \n",
       "3  Tide Original liquid laundry detergent provide...                1791  \n",
       "4  Nuts are the heroes of wholesome snacking. And...                3225  \n",
       "5  View larger Product Description Dinner is over...                1467  \n",
       "6  A full-bodied black tea made from ingredients ...                1415  \n",
       "7  Toms of Maine Goodness Day Mt. Agamenticus loc...                1403  \n",
       "8  Pure Protein 23 Grams, Vanilla Creme(4 Count, ...                1429  \n",
       "9  2g Net carbs. Taste of the tropics. Share this...                2600  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_sims_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3520c0b2",
   "metadata": {},
   "source": [
    "We can see here, the SIMS model returns popular items. Which is not optimal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48a2ee7",
   "metadata": {},
   "source": [
    "#### Similar Items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "16d0599c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ITEM_ID</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "      <th>INTERACTIONS_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00JGZBTCI</td>\n",
       "      <td>Downy</td>\n",
       "      <td>8.47</td>\n",
       "      <td>Downy April Fresh Liquid Fabric Conditioner so...</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B00MTSGYG0</td>\n",
       "      <td>Bounce</td>\n",
       "      <td>8.24</td>\n",
       "      <td>We admit, the outdoors smell great. But with o...</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B00UBJQHMU</td>\n",
       "      <td>Listerine</td>\n",
       "      <td>5.99</td>\n",
       "      <td>Protect your mouth from germs and get fresh br...</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B011DX5G92</td>\n",
       "      <td>Charmin</td>\n",
       "      <td>6.99</td>\n",
       "      <td>Charmin Ultra Strong toilet paper cleans bette...</td>\n",
       "      <td>536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B019XEY5GS</td>\n",
       "      <td>Bounty</td>\n",
       "      <td>14.99</td>\n",
       "      <td>Ever try one of those bargain' paper towels an...</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B00HZYDW5E</td>\n",
       "      <td>Coca-Cola</td>\n",
       "      <td>4.68</td>\n",
       "      <td></td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B00PC5CI8K</td>\n",
       "      <td>Rice Krispies Treats</td>\n",
       "      <td>3.68</td>\n",
       "      <td>Make snack time a little sweeter with Snap, Cr...</td>\n",
       "      <td>737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B015TYDZTE</td>\n",
       "      <td>Bounty</td>\n",
       "      <td>11.42</td>\n",
       "      <td>Ever try one of those bargain' paper towels an...</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B00JGZCLIY</td>\n",
       "      <td>Downy</td>\n",
       "      <td>7.94</td>\n",
       "      <td>No dyes, no perfumes, no worries: Downy Fabric...</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0010XS2CE</td>\n",
       "      <td>Softsoap</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Experience the soothing fragrance of lavender ...</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ITEM_ID                 BRAND  PRICE  \\\n",
       "0  B00JGZBTCI                 Downy   8.47   \n",
       "1  B00MTSGYG0                Bounce   8.24   \n",
       "2  B00UBJQHMU             Listerine   5.99   \n",
       "3  B011DX5G92               Charmin   6.99   \n",
       "4  B019XEY5GS                Bounty  14.99   \n",
       "5  B00HZYDW5E             Coca-Cola   4.68   \n",
       "6  B00PC5CI8K  Rice Krispies Treats   3.68   \n",
       "7  B015TYDZTE                Bounty  11.42   \n",
       "8  B00JGZCLIY                 Downy   7.94   \n",
       "9  B0010XS2CE              Softsoap   2.99   \n",
       "\n",
       "                                         DESCRIPTION  INTERACTIONS_COUNT  \n",
       "0  Downy April Fresh Liquid Fabric Conditioner so...                 544  \n",
       "1  We admit, the outdoors smell great. But with o...                 394  \n",
       "2  Protect your mouth from germs and get fresh br...                 483  \n",
       "3  Charmin Ultra Strong toilet paper cleans bette...                 536  \n",
       "4  Ever try one of those bargain' paper towels an...                 483  \n",
       "5                                                                    408  \n",
       "6  Make snack time a little sweeter with Snap, Cr...                 737  \n",
       "7  Ever try one of those bargain' paper towels an...                 606  \n",
       "8  No dyes, no perfumes, no worries: Downy Fabric...                 278  \n",
       "9  Experience the soothing fragrance of lavender ...                 818  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendations_similar_items_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6dbe33",
   "metadata": {},
   "source": [
    "Let's print the descriptions too see if they match our item theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7cc8c340",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----ITEM----\n",
      "Recommendation number 1, BRAND: Downy, PRICE: 8.47\n",
      "----DESCRIPTION----\n",
      "Downy April Fresh Liquid Fabric Conditioner softens, freshens, and protects your clothes from stretching, fading, and fuzzleaving them with a long-lasting Febreze-fresh floral scent youll want to keep on sniffing. This conditioning fabric softener fights static and reduces more wrinkles than using detergent alone in the wash. Easy to use and compatible with top- and front-loading machines, Downy is the must-have addition to laundry dayso your clothes can always look and feel their best. View larger View larger View larger Shaken or stirred: Your clothes can get whipped 3,000x every time you wash them. We have liftoff: The spin cycle crushes fabrics with 60x the g-force of rocket launch. Now we're cooking. The dryer can reach temperatures hot enough to cook ribs. View larger View larger View larger A little bit of Downy goes a long way: Pour according to the size of your load. Where there's a washer, there's a way: You can use Downy in your topor front-loading machine. With Downy, your clothes will look and feel great for brighter, softer fabrics (vs. using detergent alone).\n",
      "----ITEM----\n",
      "Recommendation number 2, BRAND: Bounce, PRICE: 8.24\n",
      "----DESCRIPTION----\n",
      "We admit, the outdoors smell great. But with our Bounce Outdoor Fresh fabric softener dryer sheets you also get fewer wrinkles, way less static cling, repel lint and hair while keeping your fabrics soft - all on top of an outdoor fresh scent. Ahhhhh, now thats a breath of fresh air. View larger View larger View larger View larger\n",
      "----ITEM----\n",
      "Recommendation number 3, BRAND: Listerine, PRICE: 5.99\n",
      "----DESCRIPTION----\n",
      "Protect your mouth from germs and get fresh breath with Listerine Cool Mint Antiseptic Mouthwash. It kills 99.9% of germs that cause bad breath, plaque and gingivitis, for a fresher and cleaner mouth than brushing alone. Accepted by the American Dental Association, this germ-killing mouthwash is clinically shown to reduce plaque 52 percent more and reduce gingivitis 21 percent more than brushing and flossing alone. Adding a 30-second rinse to your morning and evening routines is all it takes to get 24-hour germ protection and achieve maximum results. Listerine Cool Mint Antiseptic Mouthwash leaves your mouth feeling intensely clean. View larger View larger View larger Listerine is the next step to ensuring a healthier mouth. Focusing on good oral care habits, and sticking to them, includes regular visits to your dentist, and a daily regimen of brushing, flossing, and rinsing with Listerine. The truth is that brushing reaches 25% of your mouth. Listerine cleans virtually 100%. The power of the swish fights bad breath bacteria in your whole mouth, including on your teeth, tongue, and gums. Listerine mouthwash should be used twice a day to reach its deep-clean effectiveness. After all, bad breath germs are in your mouth all day, every day, and rinsing twice a day provides 24-hour germ protection. For Listerine rinse to reach its full powerful potential, use as directed and rinse vigorously for 30 seconds twice daily. With over 35 years of clinical support, Listerine is the world's most clinically researched mouthwash brand*. Its bold tingle has been America's favorite. Listerine Antiseptic the key to getting your whole mouth clean. Youll get 24-hour germ protection with twice daily usage, and clinically proven oral health benefits. Listerine Antiseptic has the power to kill germs that lead to bad breath, plaque and gum disease, 21% greater gingivitis reduction than brushing and flossing alone, and greater reach than brushing and flossing alone. Listerine Antiseptic provides virtually 100% coverage on teeth, between teeth and along the gum line, and is the only nationally branded antimicrobial mouthwash to receive the ADA seal of acceptance for fighting plaque and gingivitis. *Panorama 2015 What happens each time you swish properly? Research shows that antimicrobial Listerine mouthwash kills oral bacteria within 30 seconds for a difference you will notice with continued use. In fact, the bacteria in plaque and saliva samples were killed within 30 seconds of coming into contact with Listerine rinse. Dont give germs a fighting chance. Swish the full 30 seconds to make sure youre getting a truly clean mouth. Gingivitis is a common and mild form of gum disease that is a precursor to the more serious gum disease called periodontitis. Gingivitis can be so mild, in fact, that you may not even recognize it. Gingivitis is caused by the buildup of bacteria in our mouths, which combine into a sticky substance called plaque. Plaque infects and inflames the gums, developing into gingivitis. Symptoms may include redness and swelling of gums and bleeding while brushing. How can you prevent gingivitis? The best way to prevent gingivitis is to maintain a healthy oral care routine, including brushing twice a day, flossing once a day, and rinsing twice a day. Good oral care habits keep your mouth clean and healthy; remove plaque, maintain a healthy diet, and see your dentist at least once every six months.\n",
      "----ITEM----\n",
      "Recommendation number 4, BRAND: Charmin, PRICE: 6.99\n",
      "----DESCRIPTION----\n",
      "Charmin Ultra Strong toilet paper cleans better versus the leading bargain brand with fewer sheets. Its washcloth-like texture is stronger* and you can use less versus the leading bargain brand. When you buy Charmin Ultra Strong Mega Roll toilet paper you get a big, long-lasting roll because one Charmin Mega Roll equals 4 Regular Rolls**. We all go to the bathroom, those who go with Charmin Ultra Strong toilet paper really Enjoy the Go! (*when wet vs. the leading bargain brand; **Based on number of sheets in Charmin Regular Roll bath tissue)\n",
      "----ITEM----\n",
      "Recommendation number 5, BRAND: Bounty, PRICE: 14.99\n",
      "----DESCRIPTION----\n",
      "Ever try one of those bargain' paper towels and go through so many sheets that the roll just disappears? With Bounty, the roll doesnt disappear. Bounty Select-A-Size paper towels are 2X more absorbent,* so you can use less. Bounty quickly absorbs messes, so you can clean up and move on without wasting time or towels. Plus, it comes in a variety of sizes and prints, so you can choose the Bounty paper towel that's just right for you and your family. Choose the paper towel you can count on: Bounty, the longer-lasting Quicker Picker Upper.** *Vs. the Custom-Size-It brand in the U.S. and the Premium Green Leaf Towel in Canada. **Vs. leading ordinary brand. Dont be let down by bargain brand paper towels that tear and disappear in days. Try Bounty instead. Bounty is 2x more absorbent,* so you can pick up messes with as little as one sheet. And that means you dont have to keep pulling on a roll of Bounty, because it can last 50% longer. You'll spend more time doing the things you want and less time worrying about running out. *Vs. the leading ordinary brand. View larger View larger View larger Bounty paper towels are so absorbent you can use them for a range of unexpected tasks, such as cleaning makeup brushes and jewelry. Bounty paper towels are so durable you can use them in place of dish rags or sponges. Dish sponges and rags leave a germy residue everywhere they touch. With Bounty, your durable paper towel goes right in the trash when youre done. And it's 3x cleaner!* *Than a germy dishcloth.\n",
      "----ITEM----\n",
      "Recommendation number 6, BRAND: Coca-Cola, PRICE: 4.68\n",
      "----DESCRIPTION----\n",
      "\n",
      "----ITEM----\n",
      "Recommendation number 7, BRAND: Rice Krispies Treats, PRICE: 3.68\n",
      "----DESCRIPTION----\n",
      "Make snack time a little sweeter with Snap, Crackle, Pop, and the irresistible taste of Rice Krispies Treats Crispy Marshmallow Squares Original, the crispy treat that's sure to satisfy. Each marshmallow square is made with puffed rice cereal and the taste of soft, gooey marshmallows for a delicious, ready-to-eat treat wherever you go. A classically delicious snack, each package contains one individually wrapped marshmallow bar that's perfect for snacks at school, the office, traveling, lunchtime, at the game, and more. Satisfy your sweet craving with the simple goodness and on-the-go convenience of Kellogg's Rice Krispies Treats Marshmallow Squares Original. From one great day over 100 years ago all the way to today, Kellogg's has continued to fuel better days for American families. From going to the moon to feeding the U.S. Army to making your days great, some of our best days have been in your home country, the United States.\n",
      "----ITEM----\n",
      "Recommendation number 8, BRAND: Bounty, PRICE: 11.42\n",
      "----DESCRIPTION----\n",
      "Ever try one of those bargain' paper towels and go through so many sheets that the roll just disappears? With Bounty, the roll doesnt disappear. Bounty paper towels are 2X more absorbent,* so the roll can last up to 50% longer,* Bounty quickly absorbs messes, so you can clean up and move on without wasting time or towels. Plus, it comes in a variety of sizes and prints, including Bounty Select-A-Size, so you can choose the Bounty paper towel that's best for you and your family. Choose the paper towel you can count on: Bounty, the longer-lasting Quicker Picker Upper.* *Vs. leading ordinary brand. Dont be let down by bargain brand paper towels that tear and disappear in days. Try Bounty instead. Bounty is 2x more absorbent,* so you can pick up messes with as little as one sheet. And that means you dont have to keep pulling on a roll of Bounty, because it can last 50% longer. You'll spend more time doing the things you want and less time worrying about running out. *vs. the leading ordinary brand. Bountys prints are also available in Bounty Napkins. So you can add some fun to the dinner table. Bounty Prints are sold in an assortment of prints for a surprise in every pack!\n",
      "----ITEM----\n",
      "Recommendation number 9, BRAND: Downy, PRICE: 7.94\n",
      "----DESCRIPTION----\n",
      "No dyes, no perfumes, no worries: Downy Fabric Conditioner is hypoallergenic and dermatologist-testedperfect for those with sensitive skin. Made with the same Downy conditioning protection you know and love, Downy Free & Gentle helps prevent clothes from stretching, fading, and fuzz. So you can enjoy a gentler, scentless softness for all your fabrics. View larger View larger View larger Shaken or stirred: Your clothes can get whipped 3,000x every time you wash them. We have liftoff: The spin cycle crushes fabrics with 60x the g-force of a rocket launch. Now we're cooking, The dryer can reach temperatures hot enough to cook ribs. View larger View larger View larger A little bit of Downy Free & Gentle goes a long way: Pour according to the size of your load. Where there's a washer, there's a way. You can use Downy in your topor front-loading machine. With Downy Free & Gentle fabric softener for sensitive skinyour clothes will look and feel great.\n",
      "----ITEM----\n",
      "Recommendation number 10, BRAND: Softsoap, PRICE: 2.99\n",
      "----DESCRIPTION----\n",
      "Experience the soothing fragrance of lavender and chamomile as it works its magic on your hands. Wash away dirt and bacteria with Softsoap liquid hand soap gentle cleansing formula. The U.S. Center for Disease Control and Prevention states that keeping hands clean is one of the most important steps we can take. It is best to wash hands with soap and clean running water for 20 seconds. Trust the clean. Wow your senses.\n"
     ]
    }
   ],
   "source": [
    "for index, row in recommendations_similar_items_df.iterrows():\n",
    "    print('----ITEM----')\n",
    "    print('Recommendation number {index}, BRAND: {brand}, PRICE: {price}'.format(index=index+1, brand=row['BRAND'], price=row['PRICE']))\n",
    "    print('----DESCRIPTION----')\n",
    "    print(row['DESCRIPTION'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2575d9c",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "As we can see from the examples above, the new recipe `aws-similar-items` is able to infer on item similarities including their metadata information. This is a big improvement to unlock additional signals to your similar items deployments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef7e6c82",
   "metadata": {},
   "source": [
    "### Shutdown resources\n",
    "\n",
    "Since the campaigns created in this notebook will generate ongoing changes, the following logic will initiate the process of deleting them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8fc3c6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '8c8f1f5a-f58b-425c-a172-c7743100ca7d',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'date': 'Tue, 20 Dec 2022 23:49:13 GMT',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '0',\n",
       "   'connection': 'keep-alive',\n",
       "   'x-amzn-requestid': '8c8f1f5a-f58b-425c-a172-c7743100ca7d'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personalize.delete_campaign(campaignArn = sims_solution['campaign_arn'])\n",
    "personalize.delete_campaign(campaignArn = similar_items_solution['campaign_arn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efbcbc29",
   "metadata": {},
   "source": [
    "Other resources created in this notebook such as solutions, solution versions, and datasets do not incur ongoing charges. They can be deleted from the Personalize console. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1633c7cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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