「sample_content_based_ipynb」的副本.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nicochang7631/data-course-sample/blob/main/%E3%80%8Csample_content_based_ipynb%E3%80%8D%E7%9A%84%E5%89%AF%E6%9C%AC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84de56a8",
      "metadata": {
        "id": "84de56a8"
      },
      "source": [
        "# Sample Code"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rake-nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YYVyt4Psqp1",
        "outputId": "050749e5-66d3-47ec-8f03-ad3b6e3df4e8"
      },
      "id": "0YYVyt4Psqp1",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rake-nltk in /usr/local/lib/python3.7/dist-packages (1.0.6)\n",
            "Requirement already satisfied: nltk<4.0.0,>=3.6.2 in /usr/local/lib/python3.7/dist-packages (from rake-nltk) (3.6.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (1.1.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (2021.11.10)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake-nltk) (4.62.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk_stopwords = nltk.corpus.stopwords.words('english')\n",
        "print('NLTK has {} stop words'.format(len(nltk_stopwords)))\n",
        "print('The first five stop words are {}'.format(list(nltk_stopwords)[:5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SM552gZjIR6J",
        "outputId": "9e096c4e-79c4-43c3-908d-0fadb5926542"
      },
      "id": "SM552gZjIR6J",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NLTK has 179 stop words\n",
            "The first five stop words are ['i', 'me', 'my', 'myself', 'we']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "688675f5",
      "metadata": {
        "id": "688675f5"
      },
      "source": [
        "## 基礎建設"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "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": 6,
      "id": "9402e9c7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9402e9c7",
        "outputId": "5751896f-9eb2-47ca-b3f4-d64aab82ec3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-01-02 15:54:08--  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.5’\n",
            "\n",
            "All_Beauty.csv.5    100%[===================>]  14.78M  42.1MB/s    in 0.4s    \n",
            "\n",
            "2022-01-02 15:54:08 (42.1 MB/s) - ‘All_Beauty.csv.5’ saved [15499476/15499476]\n",
            "\n",
            "--2022-01-02 15:54:08--  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.5’\n",
            "\n",
            "meta_All_Beauty.jso 100%[===================>]   9.85M  31.7MB/s    in 0.3s    \n",
            "\n",
            "2022-01-02 15:54:09 (31.7 MB/s) - ‘meta_All_Beauty.json.gz.5’ 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": 7,
      "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": 8,
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        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>tech1</th>\n",
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              "      <th>1</th>\n",
              "      <td>[]</td>\n",
              "      <td></td>\n",
              "      <td>[No7 Lift &amp; Luminate Triple Action Serum 50ml ...</td>\n",
              "      <td></td>\n",
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              "      <td>[B01E7LCSL6, B008X5RVME]</td>\n",
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              "      <td>[No7 Stay Perfect Foundation now stays perfect...</td>\n",
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              "      <td>No7 Stay Perfect Foundation Cool Vanilla by No7</td>\n",
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              "      <td></td>\n",
              "      <td>No7</td>\n",
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              "      <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",
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              "      <td>Wella Koleston Perfect Hair Colour 44/44 Mediu...</td>\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",
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              "[5 rows x 19 columns]"
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          "metadata": {},
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      "source": [
        "metadata.head()"
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    },
    {
      "cell_type": "code",
      "source": [
        "#刪除重複的asin\n",
        "import string\n",
        "import re\n",
        "import nltk\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "df = metadata.drop_duplicates('title')\n",
        "df = df.drop_duplicates('asin')\n",
        "df = df[['asin','rank','title']]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UUj_Fz2v8jBb",
        "outputId": "d0f4502d-aa67-488a-83b9-41f02d9c86cf"
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      "id": "UUj_Fz2v8jBb",
      "execution_count": 9,
      "outputs": [
        {
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7367905066</td>\n",
              "      <td>1,870,258 in Beauty &amp; Personal Care (</td>\n",
              "      <td>Wella Koleston Perfect Hair Colour 44/44 Mediu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>Lacto Calamine Skin Balance Oil control 120 ml...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7413bba1-3a7a-4fc5-beb7-3af5dea99983')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\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",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      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-7413bba1-3a7a-4fc5-beb7-3af5dea99983 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-7413bba1-3a7a-4fc5-beb7-3af5dea99983');\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  ...                                              title\n",
              "0  6546546450  ...      Loud 'N Clear&trade; Personal Sound Amplifier\n",
              "1  7178680776  ...  No7 Lift &amp; Luminate Triple Action Serum 50...\n",
              "2  7250468162  ...    No7 Stay Perfect Foundation Cool Vanilla by No7\n",
              "3  7367905066  ...  Wella Koleston Perfect Hair Colour 44/44 Mediu...\n",
              "4  7414204790  ...  Lacto Calamine Skin Balance Oil control 120 ml...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['title'] = df['title'].str.lower()\n",
        "#punctuation_table = str.maketrans('', '', string.punctuation)\n",
        "#df['title'] = df['title'].str.translate(punctuation_table)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n6o2dDKPGn9K",
        "outputId": "0c1c8254-20f9-49ff-8430-583233a5d080"
      },
      "id": "n6o2dDKPGn9K",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f2a35f82-e71a-4728-a8e2-fc0d86cfcf2a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>rank</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6546546450</td>\n",
              "      <td>2,938,573 in Beauty &amp; Personal Care (</td>\n",
              "      <td>loud 'n clear&amp;trade; personal sound amplifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7178680776</td>\n",
              "      <td>872,854 in Beauty &amp; Personal Care (</td>\n",
              "      <td>no7 lift &amp;amp; luminate triple action serum 50...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7250468162</td>\n",
              "      <td>956,696 in Beauty &amp; Personal Care (</td>\n",
              "      <td>no7 stay perfect foundation cool vanilla by no7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7367905066</td>\n",
              "      <td>1,870,258 in Beauty &amp; Personal Care (</td>\n",
              "      <td>wella koleston perfect hair colour 44/44 mediu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7414204790</td>\n",
              "      <td>67,701 in Beauty &amp; Personal Care (</td>\n",
              "      <td>lacto calamine skin balance oil control 120 ml...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2a35f82-e71a-4728-a8e2-fc0d86cfcf2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\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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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "      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-f2a35f82-e71a-4728-a8e2-fc0d86cfcf2a 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-f2a35f82-e71a-4728-a8e2-fc0d86cfcf2a');\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  ...                                              title\n",
              "0  6546546450  ...      loud 'n clear&trade; personal sound amplifier\n",
              "1  7178680776  ...  no7 lift &amp; luminate triple action serum 50...\n",
              "2  7250468162  ...    no7 stay perfect foundation cool vanilla by no7\n",
              "3  7367905066  ...  wella koleston perfect hair colour 44/44 mediu...\n",
              "4  7414204790  ...  lacto calamine skin balance oil control 120 ml...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "stopwords = set(nltk.corpus.stopwords.words('english') + ['reuter', '\\x03'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OurUrD0CK6ee",
        "outputId": "cf3ab8a3-d94c-4fb7-d29f-eb11591d2e04"
      },
      "id": "OurUrD0CK6ee",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['title'] = [re.sub(r'\\d+', 'num', string) for string in df['title']]\n"
      ],
      "metadata": {
        "id": "CwNfF94FPM1u"
      },
      "id": "CwNfF94FPM1u",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopwords(string):\n",
        "  remove_num=''\n",
        "  for word in string.split():\n",
        "    if word not in stopwords:\n",
        "      remove_num = remove_num + \" \" + word\n",
        "    return remove_num\n",
        "\n",
        "df['title'] = [remove_stopwords(string) for string in df['title']]\n"
      ],
      "metadata": {
        "id": "e6gMTz2XLQrQ"
      },
      "id": "e6gMTz2XLQrQ",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "YMTtYDNTt_aM"
      },
      "id": "YMTtYDNTt_aM",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = pd.Series(df.index,index = df['title'])\n",
        "def recommend_item(item_input, k=2):\n",
        "    try:\n",
        "        item_index = mapping[item_input]\n",
        "        similarity_score = list(enumerate(similarity_matrix[item_index]))\n",
        "        similarity_score = sorted(similarity_score, key=lambda x: x[1], reverse=True)\n",
        "        similarity_score = similarity_score[:k]\n",
        "        item_indices = [i[0] for i in similarity_score]\n",
        "        return (df['asin'].iloc[item_indices].tolist())\n",
        "    except:\n",
        "        return []"
      ],
      "metadata": {
        "id": "pf5M6Th8xAsu"
      },
      "id": "pf5M6Th8xAsu",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "695362e3",
      "metadata": {
        "id": "695362e3"
      },
      "source": [
        "## 資料整理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "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": 17,
      "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": 18,
      "id": "61e7487b",
      "metadata": {
        "id": "61e7487b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d96e5c38-4009-4d39-8f48-ac3cc52ed3f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:336: UserWarning: \"https://images-na.ssl-images-amazon.com/images/I/41BevqmllOL.jpg\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
            "  ' that document to Beautiful Soup.' % decoded_markup\n",
            "/usr/local/lib/python3.7/dist-packages/bs4/__init__.py:273: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
            "  ' Beautiful Soup.' % markup)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from bs4 import BeautifulSoup\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#description\n",
        "metadata['description'] = metadata.description.apply(lambda x: BeautifulSoup(x[0]).text if x != [] else '')\n",
        "df = metadata.drop_duplicates('description').reset_index(drop=True)\n",
        "tf = TfidfVectorizer(analyzer='word')\n",
        "tfidf_matrix = tf.fit_transform(df['description'])\n",
        "\n",
        "# 計算商品用標題所表示的 tfidf 矩陣\n",
        "df = metadata.drop_duplicates('title')\n",
        "tf = TfidfVectorizer(analyzer='word')\n",
        "tfidf_matrix = tf.fit_transform(df['title'])\n",
        "\n",
        "# 計算商品間的相似程度\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "similarity_matrix = cosine_similarity(tfidf_matrix)\n",
        "mapping = pd.Series(df.index,index = df['asin'])\n",
        "\n",
        "# 每個商品回傳 k 個最相近的商品\n",
        "def recommend_item(item_input, k=2):\n",
        "    try:\n",
        "        item_index = mapping[item_input]\n",
        "        similarity_score = list(enumerate(similarity_matrix[item_index]))\n",
        "        similarity_score = sorted(similarity_score, key=lambda x: x[1], reverse=True)\n",
        "        similarity_score = similarity_score[:k]\n",
        "        item_indices = [i[0] for i in similarity_score]\n",
        "        return (df['asin'].iloc[item_indices].tolist())\n",
        "    except:\n",
        "        return []\n",
        "\n",
        "# 利用使用者購買過的商品產生推薦\n",
        "def recommend_items(items, k):\n",
        "    res = []\n",
        "    for d in items:\n",
        "        res.extend(recommend_item(d, k))\n",
        "    return res\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "9280a043",
      "metadata": {
        "id": "9280a043",
        "outputId": "8c52c9b8-5270-409e-8856-c44569ade0d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "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",
        "    content-based\n",
        "    '''\n",
        "    ratings_trainings = training_data\n",
        "    recommendations = {user: recommend_items(metadata[metadata['asin'].isin(ratings_trainings[ratings_trainings['reviewerID'] == user]['asin'].tolist())]['description'].tolist(), k) for user in users}\n",
        "    return recommendations\n",
        "\n",
        "ratings_by_user = recommender(ratings_trainings, users)\n",
        "ratings_by_user"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04a4ca9e",
      "metadata": {
        "id": "04a4ca9e"
      },
      "source": [
        "## 結果評估"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
        "evaluate(ratings_testings_by_user, ratings_by_user)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53055ac2",
      "metadata": {
        "id": "53055ac2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.0"
    },
    "colab": {
      "name": "「sample-content-based.ipynb」的副本",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}