what_to_buy.ipynb

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        "<!DOCTYPE html>\n",
        "<html lang=\"en\">\n",
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        "<meta charset=\"UTF-8\">\n",
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        "<body>\n",
        "    <h1>Zahra Alinaghipour</h1>\n",
        "</body>\n",
        "</html>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Recommender Systems\n",
        "\n",
        "Using a deep learning architecture, we implemented a model that can estimate a user's interest in a specific item. With this model, we can generate a list of items to recommend to the user that they have not purchased yet, but according to the patterns the model has found between users and items, they are likely to enjoy and rate highly. The dataset for this project includes real data related to electronic products available on the online store <a href=\"https://Amazon.com/\" target=\"_blank\">Amazon</a>.\n",
        "\n",
        "Although this project deals with online store data, this recommender model can be applied to various domains. For example, a similar system can be designed to recommend movies or music tracks to users."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The training data is located in the file amazon_train.csv. This file contains 4 columns, which respectively represent the user ID, product ID, the rating given by the user to the product, and the timestamp of the rating. The network I implemented takes the numerical IDs of the user and the product as input and predicts the rating that the user would give to that product."
      ]
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      "execution_count": 2,
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        "id": "sobudbMZhOi3"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "train = pd.read_csv('./data/amazon_train.csv')\n",
        "valid = pd.read_csv('./data/amazon_valid.csv')\n",
        "test = pd.read_csv('./data/amazon_test.csv')"
      ]
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            "text/plain": [
              "           UserID   ProductID  Rating   Timestamp\n",
              "0  A3HICVLF4PFFMN  0594481813     5.0  2014-05-05\n",
              "1  A2QBZA4S1ROX9Q  0594481813     3.0  2013-05-25\n",
              "2   AT09WGFUM934H  0594481813     3.0  2013-08-31\n",
              "3   AGAKHE014LQFU  0594481813     3.0  2013-09-18\n",
              "4  A1S6B5QFWGVL5U  0594481813     4.0  2013-06-27"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 909201 entries, 0 to 909200\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   UserID     909201 non-null  object \n",
            " 1   ProductID  909201 non-null  object \n",
            " 2   Rating     909201 non-null  float64\n",
            " 3   Timestamp  909201 non-null  object \n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 27.7+ MB\n"
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              "Rating\n",
              "5.0    0.598494\n",
              "4.0    0.212489\n",
              "3.0    0.084920\n",
              "1.0    0.057540\n",
              "2.0    0.046558\n",
              "Name: proportion, dtype: float64"
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c270abd5-f091-4418-8539-1541549a0a2c 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-c270abd5-f091-4418-8539-1541549a0a2c');\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",
              "\n",
              "\n",
              "<div id=\"df-243e651f-7206-4ab7-92b9-0ccf0c45a23f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-243e651f-7206-4ab7-92b9-0ccf0c45a23f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-243e651f-7206-4ab7-92b9-0ccf0c45a23f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           UserID   ProductID  Rating\n",
              "0  A3HICVLF4PFFMN  0594481813     5.0\n",
              "1  A2QBZA4S1ROX9Q  0594481813     3.0\n",
              "2   AT09WGFUM934H  0594481813     3.0\n",
              "3   AGAKHE014LQFU  0594481813     3.0\n",
              "4  A1S6B5QFWGVL5U  0594481813     4.0"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "sampling_strategy = {1: round(train['Rating'].value_counts()[1]*2.5),\n",
        "                     2: round(train['Rating'].value_counts()[2]*3),\n",
        "                     3: round(train['Rating'].value_counts()[3]*2)}\n",
        "\n",
        "ros = RandomOverSampler(sampling_strategy=sampling_strategy, random_state=42)\n",
        "X_train_ros, y_train_ros = ros.fit_resample(train[['UserID', 'ProductID']], train['Rating'])\n",
        "\n",
        "train_ros = pd.DataFrame(X_train_ros, columns=['UserID', 'ProductID'])\n",
        "train_ros['Rating'] = y_train_ros\n",
        "\n",
        "train_ros.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-BFI7rNrlJ0",
        "outputId": "1f5d8eb2-4678-4cc9-df09-190897810108"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Rating\n",
              "5.0    0.473362\n",
              "4.0    0.168062\n",
              "3.0    0.134330\n",
              "1.0    0.113774\n",
              "2.0    0.110472\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_ros['Rating'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_KWBtBg3szTF"
      },
      "outputs": [],
      "source": [
        "train = train_ros"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "NoRc6e2o1bHB"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le_user = LabelEncoder()\n",
        "train['UserEnc'] = le_user.fit_transform(train['UserID'].values)\n",
        "\n",
        "le_product = LabelEncoder()\n",
        "train['ProductEnc'] = le_product.fit_transform(train['ProductID'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zAOsilFwn6Kr",
        "outputId": "e1b4ec55-eb54-4f29-9667-ab6b9f5a81c9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-0e783f25-c781-434b-9c20-9bb9e3fd1c6f\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UserID</th>\n",
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              "      <th>ProductEnc</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AT09WGFUM934H</td>\n",
              "      <td>0594481813</td>\n",
              "      <td>3.0</td>\n",
              "      <td>85549</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AGAKHE014LQFU</td>\n",
              "      <td>0594481813</td>\n",
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              "      <td>77047</td>\n",
              "      <td>0</td>\n",
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              "      <td>A1S6B5QFWGVL5U</td>\n",
              "      <td>0594481813</td>\n",
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              "      <td>0</td>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e783f25-c781-434b-9c20-9bb9e3fd1c6f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e783f25-c781-434b-9c20-9bb9e3fd1c6f 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-0e783f25-c781-434b-9c20-9bb9e3fd1c6f');\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",
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              "\n",
              "\n",
              "<div id=\"df-adc03fa0-7a36-446a-adfc-c373a94640b9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-adc03fa0-7a36-446a-adfc-c373a94640b9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-adc03fa0-7a36-446a-adfc-c373a94640b9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           UserID   ProductID  Rating  UserEnc  ProductEnc\n",
              "0  A3HICVLF4PFFMN  0594481813     5.0    59277           0\n",
              "1  A2QBZA4S1ROX9Q  0594481813     3.0    41167           0\n",
              "2   AT09WGFUM934H  0594481813     3.0    85549           0\n",
              "3   AGAKHE014LQFU  0594481813     3.0    77047           0\n",
              "4  A1S6B5QFWGVL5U  0594481813     4.0    18672           0"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "euQEci6Ytamt"
      },
      "outputs": [],
      "source": [
        "valid['UserEnc'] = le_user.transform(valid['UserID'].values)\n",
        "valid['ProductEnc'] = le_product.transform(valid['ProductID'].values)\n",
        "\n",
        "test['UserEnc'] = le_user.transform(test['UserID'].values)\n",
        "test['ProductEnc'] = le_product.transform(test['ProductID'].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8WAJmLCc1hP5",
        "outputId": "91b5bc21-753f-460b-9ef8-6d69886b9e9a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "train"
            },
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              "      <th>UserID</th>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-635dead8-52e4-4266-88bc-5ed9c8deb395');\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",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23a704c0-9bf6-4c22-afe8-3e81793ad8ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           UserID   ProductID  Rating  UserEnc  ProductEnc  RatingNorm\n",
              "0  A3HICVLF4PFFMN  0594481813     5.0    59277           0        1.00\n",
              "1  A2QBZA4S1ROX9Q  0594481813     3.0    41167           0        0.50\n",
              "2   AT09WGFUM934H  0594481813     3.0    85549           0        0.50\n",
              "3   AGAKHE014LQFU  0594481813     3.0    77047           0        0.50\n",
              "4  A1S6B5QFWGVL5U  0594481813     4.0    18672           0        0.75"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Scaling the Rating column\n",
        "scaler = MinMaxScaler()\n",
        "train['RatingNorm'] = scaler.fit_transform(np.array(train['Rating']).reshape(-1, 1))\n",
        "\n",
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "jf-p3U9qwozy"
      },
      "outputs": [],
      "source": [
        "valid['RatingNorm'] = scaler.transform(np.array(valid['Rating']).reshape(-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "mqa6gyA21jvw"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "# Building the model\n",
        "\n",
        "N_USERS = train['UserEnc'].nunique()\n",
        "N_ITEMS = train['ProductEnc'].nunique()\n",
        "EMBEDDING_SIZE = 200\n",
        "\n",
        "\n",
        "# User\n",
        "input_user = keras.layers.Input(shape = (1,),\n",
        "                                dtype = 'int64',\n",
        "                                name = 'input_user')\n",
        "\n",
        "embedding_user = keras.layers.Embedding(N_USERS, EMBEDDING_SIZE,\n",
        "                                        embeddings_initializer = 'he_normal',\n",
        "                                        name = 'embedding_user')(input_user)\n",
        "\n",
        "embedding_user = keras.layers.Flatten()(embedding_user)\n",
        "\n",
        "embedding_user_bias = keras.layers.Embedding(N_USERS, 1,\n",
        "                                        embeddings_initializer = 'he_normal',\n",
        "                                        name = 'embedding_user_bias')(input_user)\n",
        "\n",
        "embedding_user_bias = keras.layers.Flatten()(embedding_user_bias)\n",
        "\n",
        "# Product\n",
        "input_product = keras.layers.Input(shape = (1,),\n",
        "                                dtype = 'int64',\n",
        "                                name = 'input_product')\n",
        "\n",
        "embedding_product = keras.layers.Embedding(N_ITEMS, EMBEDDING_SIZE,\n",
        "                                        embeddings_initializer = 'he_normal',\n",
        "                                        name = 'embedding_product')(input_product)\n",
        "\n",
        "embedding_product = keras.layers.Flatten()(embedding_product)\n",
        "\n",
        "embedding_product_bias = keras.layers.Embedding(N_ITEMS, 1,\n",
        "                                        embeddings_initializer = 'he_normal',\n",
        "                                        name = 'embedding_product_bias')(input_product)\n",
        "\n",
        "embedding_product_bias = keras.layers.Flatten()(embedding_product_bias)\n",
        "\n",
        "# User-Product Interaction and Output\n",
        "interaction = keras.layers.dot([embedding_user, embedding_product], axes = 1)\n",
        "interaction = keras.layers.add([interaction, embedding_user_bias, embedding_product_bias])\n",
        "output = keras.layers.Activation('sigmoid')(interaction)\n",
        "\n",
        "# Final model\n",
        "model = keras.models.Model([input_user, input_product], output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "S_iUXz_jhOi_"
      },
      "outputs": [],
      "source": [
        "# Training phase\n",
        "model.compile(optimizer=keras.optimizers.Adam(0.001), loss='binary_crossentropy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfDHuhbhwJI3",
        "outputId": "fe6385bb-dea9-471d-f6eb-94f21708964e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_user (InputLayer)     [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " input_product (InputLayer)  [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " embedding_user (Embedding)  (None, 1, 200)               1801000   ['input_user[0][0]']          \n",
            "                                                          0                                       \n",
            "                                                                                                  \n",
            " embedding_product (Embeddi  (None, 1, 200)               9753800   ['input_product[0][0]']       \n",
            " ng)                                                                                              \n",
            "                                                                                                  \n",
            " flatten (Flatten)           (None, 200)                  0         ['embedding_user[0][0]']      \n",
            "                                                                                                  \n",
            " flatten_2 (Flatten)         (None, 200)                  0         ['embedding_product[0][0]']   \n",
            "                                                                                                  \n",
            " embedding_user_bias (Embed  (None, 1, 1)                 90050     ['input_user[0][0]']          \n",
            " ding)                                                                                            \n",
            "                                                                                                  \n",
            " embedding_product_bias (Em  (None, 1, 1)                 48769     ['input_product[0][0]']       \n",
            " bedding)                                                                                         \n",
            "                                                                                                  \n",
            " dot (Dot)                   (None, 1)                    0         ['flatten[0][0]',             \n",
            "                                                                     'flatten_2[0][0]']           \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)         (None, 1)                    0         ['embedding_user_bias[0][0]'] \n",
            "                                                                                                  \n",
            " flatten_3 (Flatten)         (None, 1)                    0         ['embedding_product_bias[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " add (Add)                   (None, 1)                    0         ['dot[0][0]',                 \n",
            "                                                                     'flatten_1[0][0]',           \n",
            "                                                                     'flatten_3[0][0]']           \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 1)                    0         ['add[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 27902619 (106.44 MB)\n",
            "Trainable params: 27902619 (106.44 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "EFEOUHhwv546"
      },
      "outputs": [],
      "source": [
        "checkpoint_cb = keras.callbacks.ModelCheckpoint(\"model.keras\", save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "igEbOTTTwHGu",
        "outputId": "00bf21c0-c13c-4128-d049-f12bf0556489"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "17962/17962 [==============================] - 176s 10ms/step - loss: 0.5842 - val_loss: 0.5225\n",
            "Epoch 2/7\n",
            "17962/17962 [==============================] - 172s 10ms/step - loss: 0.3505 - val_loss: 0.4833\n",
            "Epoch 3/7\n",
            "17962/17962 [==============================] - 169s 9ms/step - loss: 0.2900 - val_loss: 0.4836\n",
            "Epoch 4/7\n",
            "17962/17962 [==============================] - 170s 9ms/step - loss: 0.2847 - val_loss: 0.4893\n",
            "Epoch 5/7\n",
            "17962/17962 [==============================] - 170s 9ms/step - loss: 0.2845 - val_loss: 0.4942\n",
            "Epoch 6/7\n",
            "17962/17962 [==============================] - 171s 10ms/step - loss: 0.2832 - val_loss: 0.4986\n",
            "Epoch 7/7\n",
            "17962/17962 [==============================] - 170s 9ms/step - loss: 0.2836 - val_loss: 0.5018\n"
          ]
        }
      ],
      "source": [
        "history = model.fit([train['UserEnc'], train['ProductEnc']], train['RatingNorm'],\n",
        "                    batch_size=64, epochs=7, callbacks=[checkpoint_cb],\n",
        "                    validation_data=([valid['UserEnc'], valid['ProductEnc']], valid['RatingNorm']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "q9H6bBqlzRBT",
        "outputId": "53c4cafb-7553-4acf-831a-fc094045c820"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history[\"loss\"])\n",
        "plt.plot(history.history[\"val_loss\"])\n",
        "plt.title(\"model loss\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.legend([\"train\", \"validation\"], loc=\"upper left\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "LUMGGgsW0c0W"
      },
      "outputs": [],
      "source": [
        "model = keras.models.load_model(\"model.keras\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQs_zrVVhOjA",
        "outputId": "dc6a0137-7af7-4761-a222-2680f23f263d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2810/2810 [==============================] - 5s 2ms/step\n",
            "85.09355752109057\n"
          ]
        }
      ],
      "source": [
        "# Evaluate on validation data\n",
        "from sklearn.metrics import precision_score\n",
        "\n",
        "predictions = model.predict([valid['UserEnc'], valid['ProductEnc']])\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "predictions = np.round(predictions)\n",
        "predictions[predictions < 4] = 0\n",
        "predictions[predictions >= 4] = 1\n",
        "\n",
        "true_rating = valid['Rating'].values.copy()\n",
        "true_rating[true_rating < 4] = 0\n",
        "true_rating[true_rating >= 4] = 1\n",
        "\n",
        "result = precision_score(true_rating, predictions) * 100\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTkR3fCY101k",
        "outputId": "dcadc2aa-0dbd-4b14-fd86-4ec07a90c300"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2805/2805 [==============================] - 4s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict([test['UserEnc'], test['ProductEnc']])\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "predictions = np.round(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Eo1BUN0GhOjB",
        "outputId": "02745269-066b-4fde-c6a0-f9c31cd16104"
      },
      "outputs": [
        {
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              "\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",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('submission');\n",
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              "\n",
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            ],
            "text/plain": [
              "               UserID   ProductID  Rating\n",
              "0      A2Y3A341VDK37H  B00HFI55N2     3.0\n",
              "1      A240FRPD4MEXND  B00KIMX4EY     4.0\n",
              "2      A3SBTW3WS4IQSN  B001IF252M     4.0\n",
              "3       A1QCZQTJNK3MU  B00J46VVKE     4.0\n",
              "4      A16JT7E5121OAB  B00JZC972Q     3.0\n",
              "...               ...         ...     ...\n",
              "89727  A37Z65SZVT0TVB  B00005AR4L     4.0\n",
              "89728  A3PLX6PTM2ERKL  B00004Z6PI     5.0\n",
              "89729  A3TRPVAGT3NWBS  B00005NVPW     4.0\n",
              "89730   AY8Q1X7G96HV5  B000059MSI     4.0\n",
              "89731  A3IBOQ8R44YG9L  B00004TDMX     4.0\n",
              "\n",
              "[89732 rows x 3 columns]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Preparing submission file\n",
        "submission = test.copy()\n",
        "submission['Rating'] = predictions\n",
        "submission = submission[['UserID', 'ProductID', 'Rating']]\n",
        "submission"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
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      "display_name": "Python 3",
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