rule_base.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OjeJUDxyQzi"
      },
      "source": [
        "# Sample Code"
      ],
      "id": "3OjeJUDxyQzi"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YTwl2lavyQzn"
      },
      "source": [
        "## 基礎建設"
      ],
      "id": "YTwl2lavyQzn"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "-EZAFJSeyQzn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import gzip, json\n",
        "\n",
        "def parse(path):\n",
        "    g = gzip.open(path, 'rb')\n",
        "    for l in g:\n",
        "        yield json.loads(l)\n",
        "\n",
        "def getDF(path):\n",
        "    i = 0\n",
        "    df = {}\n",
        "    for d in parse(path):\n",
        "        df[i] = d\n",
        "        i += 1\n",
        "    return pd.DataFrame.from_dict(df, orient='index')"
      ],
      "id": "-EZAFJSeyQzn"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-Rgv8yZyQzr"
      },
      "source": [
        "## 載入資料"
      ],
      "id": "B-Rgv8yZyQzr"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYSolsDvyQzs",
        "outputId": "126bc2c1-c2e8-4730-cf66-58f2ee3955c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-22 01:38:06--  http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/All_Beauty.csv\n",
            "Resolving deepyeti.ucsd.edu (deepyeti.ucsd.edu)... 169.228.63.50\n",
            "Connecting to deepyeti.ucsd.edu (deepyeti.ucsd.edu)|169.228.63.50|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 15499476 (15M) [application/octet-stream]\n",
            "Saving to: ‘All_Beauty.csv.1’\n",
            "\n",
            "All_Beauty.csv.1    100%[===================>]  14.78M  19.0MB/s    in 0.8s    \n",
            "\n",
            "2021-12-22 01:38:07 (19.0 MB/s) - ‘All_Beauty.csv.1’ saved [15499476/15499476]\n",
            "\n",
            "--2021-12-22 01:38:07--  http://deepyeti.ucsd.edu/jianmo/amazon/metaFiles2/meta_All_Beauty.json.gz\n",
            "Resolving deepyeti.ucsd.edu (deepyeti.ucsd.edu)... 169.228.63.50\n",
            "Connecting to deepyeti.ucsd.edu (deepyeti.ucsd.edu)|169.228.63.50|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10329961 (9.9M) [application/octet-stream]\n",
            "Saving to: ‘meta_All_Beauty.json.gz.1’\n",
            "\n",
            "meta_All_Beauty.jso 100%[===================>]   9.85M  15.1MB/s    in 0.7s    \n",
            "\n",
            "2021-12-22 01:38:08 (15.1 MB/s) - ‘meta_All_Beauty.json.gz.1’ saved [10329961/10329961]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/All_Beauty.csv\n",
        "!wget http://deepyeti.ucsd.edu/jianmo/amazon/metaFiles2/meta_All_Beauty.json.gz"
      ],
      "id": "oYSolsDvyQzs"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FFO9DmplyQzt"
      },
      "outputs": [],
      "source": [
        "metadata = getDF('/content/meta_All_Beauty.json.gz')\n",
        "ratings = pd.read_csv('/content/All_Beauty.csv', names=['asin', 'reviewerID', 'overall', 'unixReviewTime'], header=None)"
      ],
      "id": "FFO9DmplyQzt"
    },
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              "      <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",
              "      <th>tech2</th>\n",
              "      <th>brand</th>\n",
              "      <th>feature</th>\n",
              "      <th>rank</th>\n",
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              "      <th>details</th>\n",
              "      <th>main_cat</th>\n",
              "      <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>[Loud 'N Clear Personal Sound Amplifier allows...</td>\n",
              "      <td></td>\n",
              "      <td>Loud 'N Clear&amp;trade; Personal Sound Amplifier</td>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>idea village</td>\n",
              "      <td>[]</td>\n",
              "      <td>2,938,573 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'ASIN: ': '6546546450'}</td>\n",
              "      <td>All Beauty</td>\n",
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              "      <td>6546546450</td>\n",
              "      <td>[]</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[No7 Lift &amp; Luminate Triple Action Serum 50ml ...</td>\n",
              "      <td></td>\n",
              "      <td>No7 Lift &amp;amp; Luminate Triple Action Serum 50...</td>\n",
              "      <td>[B01E7LCSL6, B008X5RVME]</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[]</td>\n",
              "      <td>872,854 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'Shipping Weight:': '0.3 ounces (', 'ASIN: ':...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td>class=\"a-bordered a-horizontal-stripes  a-spa...</td>\n",
              "      <td></td>\n",
              "      <td>$44.99</td>\n",
              "      <td>7178680776</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[No7 Stay Perfect Foundation now stays perfect...</td>\n",
              "      <td></td>\n",
              "      <td>No7 Stay Perfect Foundation Cool Vanilla by No7</td>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>No7</td>\n",
              "      <td>[]</td>\n",
              "      <td>956,696 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[B01B8BR0O8, B01B8BR0NO, B014MHXXM8]</td>\n",
              "      <td>{'Shipping Weight:': '3.5 ounces (', 'ASIN: ':...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$28.76</td>\n",
              "      <td>7250468162</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>Wella Koleston Perfect Hair Colour 44/44 Mediu...</td>\n",
              "      <td>[B0041PBXX8]</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>[]</td>\n",
              "      <td>1,870,258 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'\n",
              "    Item Weight: \n",
              "    ': '1.76 ounces', 'Sh...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>7367905066</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>4</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[Lacto Calamine Skin Balance Daily Nourishing ...</td>\n",
              "      <td></td>\n",
              "      <td>Lacto Calamine Skin Balance Oil control 120 ml...</td>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "      <td>[]</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>{'Shipping Weight:': '12 ounces (', 'ASIN: ': ...</td>\n",
              "      <td>All Beauty</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>$12.15</td>\n",
              "      <td>7414204790</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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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "  category  ...                                    imageURLHighRes\n",
              "0       []  ...                                                 []\n",
              "1       []  ...                                                 []\n",
              "2       []  ...                                                 []\n",
              "3       []  ...  [https://images-na.ssl-images-amazon.com/image...\n",
              "4       []  ...  [https://images-na.ssl-images-amazon.com/image...\n",
              "\n",
              "[5 rows x 19 columns]"
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          "metadata": {},
          "execution_count": 32
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        "metadata.head()"
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1d56e24-f22c-4ebd-91f3-0b90e8708c6e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1d56e24-f22c-4ebd-91f3-0b90e8708c6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         asin      reviewerID  overall  unixReviewTime       DATE\n",
              "0  0143026860  A1V6B6TNIC10QE      1.0      1424304000 2015-02-19\n",
              "1  0143026860  A2F5GHSXFQ0W6J      4.0      1418860800 2014-12-18\n",
              "2  0143026860  A1572GUYS7DGSR      4.0      1407628800 2014-08-10\n",
              "3  0143026860   A1PSGLFK1NSVO      5.0      1362960000 2013-03-11\n",
              "4  0143026860   A6IKXKZMTKGSC      5.0      1324771200 2011-12-25"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "ratings.head()"
      ],
      "id": "X3xyphRByQzu"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cHAbp6y5yQzw"
      },
      "source": [
        "## 資料整理"
      ],
      "id": "cHAbp6y5yQzw"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ObBt5X1yQzx"
      },
      "outputs": [],
      "source": [
        "ratings['DATE'] = pd.to_datetime(ratings['unixReviewTime'], unit='s')"
      ],
      "id": "6ObBt5X1yQzx"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mBApvoKyQzx"
      },
      "source": [
        "## 資料切分"
      ],
      "id": "3mBApvoKyQzx"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hNiTYugXyQzy"
      },
      "outputs": [],
      "source": [
        "ratings_trainings = ratings[\n",
        "    (ratings['DATE'] < '2018-09-01')\n",
        "]\n",
        "ratings_testings = ratings[\n",
        "    (ratings['DATE'] >= '2018-09-01') & \n",
        "    (ratings['DATE'] <= '2018-09-30')\n",
        "]\n",
        "ratings_testings_by_user = ratings_testings.groupby('reviewerID').agg(list).reset_index()[['reviewerID', 'asin']].to_dict('records')\n",
        "ratings_testings_by_user = { rating['reviewerID']: rating['asin'] for rating in ratings_testings_by_user }\n",
        "users = list(ratings_testings_by_user.keys())"
      ],
      "id": "hNiTYugXyQzy"
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = ratings_trainings.merge(metadata[['asin','also_buy','also_view','brand']], on='asin')\n",
        "training_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Bj7sPOOt49z2",
        "outputId": "bc1d8035-cc72-43b6-eea9-bb23a2228f48"
      },
      "id": "Bj7sPOOt49z2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-876ade44-2119-4024-b53d-d55956d0e80b\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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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>asin</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>overall</th>\n",
              "      <th>unixReviewTime</th>\n",
              "      <th>DATE</th>\n",
              "      <th>also_buy</th>\n",
              "      <th>also_view</th>\n",
              "      <th>brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>A5TA1NJOC0PJ5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1457568000</td>\n",
              "      <td>2016-03-10</td>\n",
              "      <td>[]</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>A1TJICB7VLGQKL</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1441065600</td>\n",
              "      <td>2015-09-01</td>\n",
              "      <td>[]</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>A80M2286B7STE</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1412294400</td>\n",
              "      <td>2014-10-03</td>\n",
              "      <td>[]</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>A2TM2MAA8IT34U</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1407369600</td>\n",
              "      <td>2014-08-07</td>\n",
              "      <td>[]</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>AQGWT465GTEJG</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1402704000</td>\n",
              "      <td>2014-06-14</td>\n",
              "      <td>[]</td>\n",
              "      <td>[3254895630, B007VL1D9S, B00EH9A0RI, B0773MBG4...</td>\n",
              "      <td>Pirmal Healthcare</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-876ade44-2119-4024-b53d-d55956d0e80b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-876ade44-2119-4024-b53d-d55956d0e80b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-876ade44-2119-4024-b53d-d55956d0e80b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         asin  ...              brand\n",
              "0  7414204790  ...  Pirmal Healthcare\n",
              "1  7414204790  ...  Pirmal Healthcare\n",
              "2  7414204790  ...  Pirmal Healthcare\n",
              "3  7414204790  ...  Pirmal Healthcare\n",
              "4  7414204790  ...  Pirmal Healthcare\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwCQlGwKyQzy"
      },
      "source": [
        "## 產生推薦"
      ],
      "id": "FwCQlGwKyQzy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OeMOGY82yQzz"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "def recommender(training_data, users=[], k=10):\n",
        "    '''\n",
        "    * training_data: dataframe 輸入的訓練資料集（2018-09-01 以前資料）\n",
        "    * users: [] 需要被推薦的使用者\n",
        "    * k: int 每個使用者需要推薦的商品數\n",
        "    * recommendations: dict\n",
        "      {\n",
        "          使用者一： [推薦商品一, 推薦商品二, ...],\n",
        "          使用者二： [...], ...\n",
        "      }\n",
        "    '''\n",
        "    recommendations = {}\n",
        "    '''\n",
        "    Your Code\n",
        "    '''\n",
        "\n",
        "    # 找出5星且評論數的 top k\n",
        "    most_counts = pd.DataFrame(training_data[training_data.overall == 5].asin.value_counts()[:k])\n",
        "    most_counts.reset_index(inplace = True)\n",
        "    most_counts = most_counts['index'].to_list()\n",
        "    \n",
        "    for user in users:\n",
        "        recom = []\n",
        "        \n",
        "        # 挑出消費者給予5星評價的商品\n",
        "        recom.extend(training_data[(training_data.reviewerID == user) & (training_data.overall == 5)].asin.to_list())\n",
        "\n",
        "        # 補滿k項推薦\n",
        "        rest = k - len(recom)\n",
        "        if rest > 0:\n",
        "            # also_buy\n",
        "            # 利用 also_buy 欄位給予推薦 \n",
        "            # 若 also_buy 超過 k - len(recom) 則移除 recom 裡已有的隨機挑選做推薦\n",
        "            buy = []\n",
        "            buy.extend([i for item in training_data[(training_data.reviewerID == user) & (training_data.overall == 5)].also_buy.to_list() for i in item])\n",
        "            if len(buy) > rest:\n",
        "                recom.extend(random.choices(buy, k = rest))\n",
        "            else:\n",
        "                recom.extend(buy)\n",
        "            del buy\n",
        "\n",
        "            rest = k - len(recom)\n",
        "            if rest > 0:\n",
        "                # also_view\n",
        "                # 利用 also_view 欄位給予推薦 \n",
        "                # 若 also_view 超過 k - len(recom) 則移除 recom 裡已有的隨機挑選做推薦\n",
        "                view = []\n",
        "                view.extend([i for item in training_data[(training_data.reviewerID == user) & (training_data.overall == 5)].also_view.to_list() for i in item])\n",
        "                view = list(set(view) - set(recom))\n",
        "                if len(view) > rest:\n",
        "                    recom.extend(random.choices(view, k = rest))\n",
        "                else:\n",
        "                    recom.extend(view)\n",
        "                del view\n",
        "                \n",
        "                rest = k - len(recom)\n",
        "                if rest > 0:\n",
        "                    # brand\n",
        "                    # 利用 brand 挑出 商品 給予推薦 \n",
        "                    # 若 brand 底下的商品 超過 k - len(recom) 則移除 recom 裡已有的隨機挑選做推薦\n",
        "                    brand = training_data[(training_data.reviewerID == user) & (training_data.overall == 5)].brand.to_list()\n",
        "                    product = metadata[metadata.brand.isin(brand)].asin.to_list()\n",
        "                    product = list(set(product) - set(recom))\n",
        "                    if len(product) > rest:\n",
        "                        recom.extend(random.choices(product, k = rest))\n",
        "                    else:\n",
        "                        recom.extend(product)\n",
        "                    del product\n",
        "\n",
        "                    rest = k - len(recom)\n",
        "                    if rest > 0:\n",
        "                        # 若還是不滿系統要求的 k 個推薦\n",
        "                        # 用 5 星 且 評論數 最多的 k 個，跟 recom 取差集後，取前面 k - len(recom) 補滿推薦選項\n",
        "                        most = list(sorted(set(most_counts).difference(recom), key=lambda x:most_counts.index(x)))\n",
        "                        recom.extend(most[:rest])\n",
        "\n",
        "        recommendations[user] = recom    \n",
        "\n",
        "    return recommendations\n",
        "\n",
        "\n",
        "ratings_by_user = recommender(training_data, users)"
      ],
      "id": "OeMOGY82yQzz"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5_6-kwKyQzz"
      },
      "source": [
        "## 結果評估"
      ],
      "id": "f5_6-kwKyQzz"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRNaAEW7yQz0",
        "outputId": "bb225ef3-de31-4538-e0bd-8480c6e88748"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08305084745762711"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "def evaluate(ratings_testings_by_user={}, ratings_by_user={}, method=None):\n",
        "    '''\n",
        "    * ratings_testings_by_user: dict 真實被購買的商品資料（2018-09-01 以後資料）\n",
        "    * ratings_by_user: dict 利用訓練資料學習的推薦商品\n",
        "    * method: str\n",
        "    * score: float\n",
        "    '''\n",
        "    total = 0\n",
        "    for d in ratings_testings_by_user:\n",
        "        if d in ratings_by_user:\n",
        "            total += len(set(ratings_by_user[d]) & set(ratings_testings_by_user[d]))\n",
        "\n",
        "    score = total / len(ratings_testings)\n",
        "    return score\n",
        "\n",
        "evaluate(ratings_testings_by_user, ratings_by_user)"
      ],
      "id": "yRNaAEW7yQz0"
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
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        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "colab": {
      "name": "sample.ipynb",
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      "toc_visible": true
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