mf-lightfm.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "84de56a8",
      "metadata": {
        "id": "84de56a8"
      },
      "source": [
        "# Sample Code"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "688675f5",
      "metadata": {
        "id": "688675f5"
      },
      "source": [
        "## 基礎建設"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "b32226db",
      "metadata": {
        "scrolled": true,
        "id": "b32226db"
      },
      "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')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84ff0f9d",
      "metadata": {
        "id": "84ff0f9d"
      },
      "source": [
        "## 載入資料"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "9402e9c7",
      "metadata": {
        "id": "9402e9c7",
        "outputId": "3fe8b63d-6c6f-467f-c6ba-c2af7df03c0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-09 02:57:40--  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’\n",
            "\n",
            "All_Beauty.csv      100%[===================>]  14.78M  9.47MB/s    in 1.6s    \n",
            "\n",
            "2022-01-09 02:57:42 (9.47 MB/s) - ‘All_Beauty.csv’ saved [15499476/15499476]\n",
            "\n",
            "--2022-01-09 02:57:42--  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’\n",
            "\n",
            "meta_All_Beauty.jso 100%[===================>]   9.85M  7.54MB/s    in 1.3s    \n",
            "\n",
            "2022-01-09 02:57:43 (7.54 MB/s) - ‘meta_All_Beauty.json.gz’ 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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "84b66cc7",
      "metadata": {
        "id": "84b66cc7"
      },
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "9fd77203",
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        "id": "9fd77203",
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        "colab": {
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      "outputs": [
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          "data": {
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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",
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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",
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              "      <th>0</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[Loud 'N Clear Personal Sound Amplifier allows...</td>\n",
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              "      <td>Loud 'N Clear&amp;trade; Personal Sound Amplifier</td>\n",
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              "      <td>idea village</td>\n",
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              "      <td>[]</td>\n",
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              "      <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",
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              "      <td>872,854 in Beauty &amp; Personal Care (</td>\n",
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              "      <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",
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              "      <td>$44.99</td>\n",
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              "    <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",
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              "    <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",
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              "      <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",
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              "    <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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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  category  ...                                    imageURLHighRes\n",
              "0       []  ...                                                 []\n",
              "1       []  ...                                                 []\n",
              "2       []  ...                                                 []\n",
              "3       []  ...  [https://images-na.ssl-images-amazon.com/image...\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-15351674-bf23-4db8-bd36-4020719e1bbc 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-15351674-bf23-4db8-bd36-4020719e1bbc');\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\n",
              "0  0143026860  A1V6B6TNIC10QE      1.0      1424304000\n",
              "1  0143026860  A2F5GHSXFQ0W6J      4.0      1418860800\n",
              "2  0143026860  A1572GUYS7DGSR      4.0      1407628800\n",
              "3  0143026860   A1PSGLFK1NSVO      5.0      1362960000\n",
              "4  0143026860   A6IKXKZMTKGSC      5.0      1324771200"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "ratings.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "695362e3",
      "metadata": {
        "id": "695362e3"
      },
      "source": [
        "## 資料整理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "b552fb23",
      "metadata": {
        "id": "b552fb23"
      },
      "outputs": [],
      "source": [
        "ratings['DATE'] = pd.to_datetime(ratings['unixReviewTime'], unit='s')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "054eeba4",
      "metadata": {
        "id": "054eeba4"
      },
      "source": [
        "## 資料切分"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "284b14a8",
      "metadata": {
        "id": "284b14a8"
      },
      "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())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6c76061",
      "metadata": {
        "id": "a6c76061"
      },
      "source": [
        "## 產生推薦"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "71cb693d",
      "metadata": {
        "id": "71cb693d",
        "outputId": "cc5a1c03-e119-4531-c76c-3f71c00cb0cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lightfm\n",
            "  Downloading lightfm-1.16.tar.gz (310 kB)\n",
            "\u001b[?25l\r\u001b[K     |█                               | 10 kB 24.7 MB/s eta 0:00:01\r\u001b[K     |██▏                             | 20 kB 9.1 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 30 kB 7.6 MB/s eta 0:00:01\r\u001b[K     |████▎                           | 40 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 51 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 61 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 71 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 81 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 92 kB 3.8 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 102 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 112 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 122 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████▊                  | 133 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 143 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 153 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 163 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 174 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 184 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 194 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 204 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 215 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▎        | 225 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 235 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 245 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 256 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▌    | 266 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 276 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 286 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 296 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 307 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 310 kB 4.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.4.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from lightfm) (2.23.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lightfm) (1.0.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->lightfm) (1.24.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightfm) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightfm) (1.1.0)\n",
            "Building wheels for collected packages: lightfm\n",
            "  Building wheel for lightfm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lightfm: filename=lightfm-1.16-cp37-cp37m-linux_x86_64.whl size=705361 sha256=bd77c8a6f3b3c7748e1f584619b4abd43f7b59f84d6ccd0f64c4f0495662e853\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/56/28/5772a3bd3413d65f03aa452190b00898b680b10028a1021914\n",
            "Successfully built lightfm\n",
            "Installing collected packages: lightfm\n",
            "Successfully installed lightfm-1.16\n"
          ]
        }
      ],
      "source": [
        "!pip install lightfm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "61e7487b",
      "metadata": {
        "id": "61e7487b"
      },
      "outputs": [],
      "source": [
        "from lightfm import LightFM\n",
        "import numpy as np\n",
        "\n",
        "def recommend_items(model, data, user_id, item_list, k):\n",
        "    \n",
        "    # 取得item數量\n",
        "    n_users, n_items = data.shape\n",
        "\n",
        "    # 預測user對所有item的評分並進行推薦\n",
        "    scores = model.predict(user_id, np.arange(n_items))\n",
        "    top_items = item_list[np.argsort(-scores)]\n",
        "    return top_items[:k].tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "9280a043",
      "metadata": {
        "id": "9280a043",
        "outputId": "c9b22257-0918-46ed-c880-73553f68d1e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "from lightfm.data import Dataset\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "pd.options.mode.chained_assignment = None\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "\n",
        "def recommender(training_data, users=[], k=10, rule_based = False, add_test_user = False, add_item_feature = False, metadata = ''):\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",
        "    # 移除使用者對商品的重複評論，只留最新\n",
        "    training_data = training_data.sort_values(\"DATE\", ascending=False).groupby(['reviewerID', 'asin']).head(1)\n",
        "    recommendations = {}\n",
        "    '''\n",
        "    mf-based, lightFM\n",
        "    '''\n",
        "    # Rule based\n",
        "    if rule_based and isinstance(training_data, pd.DataFrame):\n",
        "        hot = training_data[(training_data['DATE'] >= '2018-08-01') & (training_data['DATE'] < '2018-09-01') & (training_data['overall'] >= 4)]\n",
        "        hot = pd.DataFrame(hot.asin.value_counts())\n",
        "        hot.reset_index(inplace=True)\n",
        "        hot.columns = ['asin', 'count']\n",
        "    \n",
        "    # 取出評分矩陣所需的訓練資料並以lightfm的Dataset形式存取\n",
        "    training_data = training_data[[\"reviewerID\",\"asin\",\"overall\"]]\n",
        "    dataset = Dataset()\n",
        "\n",
        "    if add_test_user:\n",
        "        dataset.fit(np.append(training_data.reviewerID.unique(), users, 0), training_data.asin.unique())\n",
        "    else:\n",
        "        dataset.fit(training_data.reviewerID.unique(), training_data.asin.unique())\n",
        "    \n",
        "    interactions_matrix, weights_matrix = dataset.build_interactions([tuple(i) for i in training_data.values])\n",
        "\n",
        "\n",
        "    # 取得user與item的mapping資料\n",
        "    user_id_map, user_feature_map, item_id_map, item_feature_map = dataset.mapping()\n",
        "    item_list = np.array(list(item_id_map.keys())).astype(object)\n",
        "\n",
        "    # 訓練模型\n",
        "    if add_item_feature:\n",
        "        assert isinstance(metadata, pd.DataFrame), \"Please input the item's data\"\n",
        "        # 篩選資料\n",
        "        metadata = metadata[metadata.asin.isin(training_data.asin.tolist())]\n",
        "        metadata.reset_index(drop=True, inplace=True)\n",
        "        \n",
        "        # set lemmatizer\n",
        "        lemmatizer = WordNetLemmatizer()\n",
        "        \n",
        "        # 處理掉description 的中括號\n",
        "        metadata['description'] = metadata['description'].apply(lambda x: ' '.join(x))\n",
        "\n",
        "        # 處理rank\n",
        "        rankdata = metadata['rank'].str.split(' ', n=1, expand=True)\n",
        "        metadata['rank_no'], metadata['rank'] = rankdata.iloc[:,0], rankdata.iloc[:,1]\n",
        "\n",
        "        # 合併文字欄位\n",
        "        metadata['all_text'] = metadata['description'] + ' ' + metadata['title'] # + ' ' + metadata['rank'] + ' ' + metadata['brand']\n",
        "\n",
        "        # 去除特殊符號，除了數字之間的\n",
        "        import re\n",
        "        regex = r\"(?<!\\d)[\\W](?!\\d)\"\n",
        "        metadata['all_text'] = metadata['all_text'].apply(lambda x: re.sub(regex, ' ', str(x)))\n",
        "        metadata['rank'] = metadata['rank'].apply(lambda x: re.sub(regex, ' ', str(x)))\n",
        "        metadata['brand'] = metadata['brand'].apply(lambda x: re.sub(regex, ' ', str(x)))\n",
        "\n",
        "        # 轉小寫\n",
        "        metadata['all_text'] = metadata['all_text'].str.lower()\n",
        "        metadata['rank'] = metadata['rank'].str.lower() \n",
        "        metadata['brand'] = metadata['brand'].str.lower()         \n",
        "\n",
        "        # remove stopword\n",
        "        def remove_stopword(sentence):\n",
        "            tokens = nltk.word_tokenize(sentence)\n",
        "            result = [lemmatizer.lemmatize(voca) for voca in tokens if not voca in stopwords.words('english')]\n",
        "            return result\n",
        "\n",
        "        metadata['all_text'] = metadata['all_text'].apply(lambda x: remove_stopword(x))\n",
        "\n",
        "        # merge column\n",
        "        for i in range(len(metadata)):\n",
        "            try:\n",
        "                metadata['all_text'][i] = metadata['all_text'][i].append(metadata['rank'][i]).append(metadata['brand'][i])\n",
        "            except:\n",
        "                metadata['all_text'][i] = []\n",
        "\n",
        "        # 建立隱性標籤\n",
        "        item_features = dataset.build_item_features([tuple(x) for x in metadata[['asin','all_text']].values])\n",
        "\n",
        "        # 訓練混合模型（Hybrid Model）加入 user / item feature\n",
        "        model = LightFM(loss='warp', item_alpha=1e-6, no_components=100)\n",
        "        model = model.fit(interactions_matrix, sample_weight = weights_matrix, item_features=item_features, epochs=20, num_threads=8)\n",
        "\n",
        "    else:\n",
        "        model = LightFM(loss='warp', item_alpha=1e-6, no_components=100)\n",
        "        model = model.fit(interactions_matrix, sample_weight = weights_matrix, epochs=20, num_threads = 8)\n",
        "\n",
        "    # 推薦\n",
        "    for user in users:\n",
        "        try:\n",
        "            recommendations[user] = recommend_items(model, interactions_matrix, user_id_map[user], item_list, k)\n",
        "        except:\n",
        "            if rule_based:\n",
        "                recommendations[user] = hot.asin[:k].tolist()\n",
        "            else:\n",
        "                recommendations[user] = []\n",
        "\n",
        "    return recommendations\n",
        "\n",
        "# 推薦成果：可看出冷啟動問題嚴重，針對未曾出現的使用者無法有鑑別的個別推薦，實務上可能會主動請使用者先評分再訓練進行推薦\n",
        "#          除此之外也能嘗試使用混合模型（Hybrid Model）加入user / item feature進行訓練"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04a4ca9e",
      "metadata": {
        "id": "04a4ca9e"
      },
      "source": [
        "## 結果評估"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ee763747",
      "metadata": {
        "id": "ee763747"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import product\n",
        "ks = [10, 20, 30]\n",
        "rule_baseds = [False, True]\n",
        "add_test_users = [False, True]\n",
        "add_item_features = [False, True]\n",
        "for k, rule_based, add_test_user, add_item_feature in product(ks, rule_baseds, add_test_users, add_item_features): \n",
        "    ratings_by_user = recommender(ratings_trainings, \n",
        "                                  users, \n",
        "                                  k=k, \n",
        "                                  rule_based = rule_based, \n",
        "                                  add_test_user = add_test_user, \n",
        "                                  add_item_feature = add_item_feature, \n",
        "                                  metadata = metadata)\n",
        "    score = round(evaluate(ratings_testings_by_user, ratings_by_user),3)\n",
        "    print(f\"推薦數 {k}, 混合 Rule-based: {str(rule_based):<5s}, 把 test user 放進模型訓練: {str(add_test_user):<5s}, 加入商品特徵: {str(add_item_feature):<5s}, recall: {score}\")"
      ],
      "metadata": {
        "id": "T-LFCPw5jUnc",
        "outputId": "60bfe6e7-3541-4f40-bb08-5fc4b062b250",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "T-LFCPw5jUnc",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "推薦數 10, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.0\n",
            "推薦數 10, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.002\n",
            "推薦數 10, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.085\n",
            "推薦數 10, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.081\n",
            "推薦數 10, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.137\n",
            "推薦數 10, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.139\n",
            "推薦數 10, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.083\n",
            "推薦數 10, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.083\n",
            "推薦數 20, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.002\n",
            "推薦數 20, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.002\n",
            "推薦數 20, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.085\n",
            "推薦數 20, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.085\n",
            "推薦數 20, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.21\n",
            "推薦數 20, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.208\n",
            "推薦數 20, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.086\n",
            "推薦數 20, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.083\n",
            "推薦數 30, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.002\n",
            "推薦數 30, 混合 Rule-based: False, 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.002\n",
            "推薦數 30, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.092\n",
            "推薦數 30, 混合 Rule-based: False, 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.095\n",
            "推薦數 30, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: False, recall: 0.239\n",
            "推薦數 30, 混合 Rule-based: True , 把 test user 放進模型訓練: False, 加入商品特徵: True , recall: 0.241\n",
            "推薦數 30, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: False, recall: 0.092\n",
            "推薦數 30, 混合 Rule-based: True , 把 test user 放進模型訓練: True , 加入商品特徵: True , recall: 0.093\n"
          ]
        }
      ]
    }
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