34_RecommendationSystem.ipynb

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      "metadata": {
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      },
      "source": [
        "<img src=\"http://akhavanpour.ir/notebook/images/srttu.gif\" alt=\"SRTTU\" style=\"width: 150px;\"/>\n",
        "\n",
        "[![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://notebooks.azure.com/import/gh/Alireza-Akhavan/class.vision)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jk0QyndIipB_"
      },
      "source": [
        "# Book Recommendation System"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTpw0OLripCA"
      },
      "source": [
        "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">سیستم توصیه گر کتاب</div>\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
        "کدها برگرفته از مستندات بلاگ :</div>\n",
        "\n",
        "[https://towardsdatascience.com/building-a-book-recommendation-system-using-keras-1fba34180699\n",
        "](https://towardsdatascience.com/building-a-book-recommendation-system-using-keras-1fba34180699)\n",
        "\n",
        "\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
        "و کدهای موجود در مخزن کد:</div>\n",
        "\n",
        "[https://github.com/TannerGilbert/Articles](https://github.com/TannerGilbert/Articles)\n",
        "\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
        "است<br>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGoRXJIZipCC"
      },
      "source": [
        "# <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">مجموعه داده</div>\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
        "کدها برگرفته از مستندات بلاگ :</div>\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">\n",
        "مجموعه داده‌ی استفاده شده در این نوت بوک   goodbooks-10k سایت Kaggle است.\n",
        "</div>\n",
        "\n",
        "[https://www.kaggle.com/zygmunt/goodbooks-10k](https://www.kaggle.com/zygmunt/goodbooks-10k)\n",
        "\n",
        "\n",
        "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">\n",
        "ویژگی های موجود در دیتاست:</div>\n",
        "\n",
        "        -book_id\n",
        "        -user_id\n",
        "        -rating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
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        "id": "MlHglnWXipCD"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.layers import Input, Embedding, Flatten, Dot, Dense, Concatenate\n",
        "from tensorflow.keras.models import Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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        "id": "enCnY1HjipCG"
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      "source": [
        "dataset = pd.read_csv('https://github.com/Alireza-Akhavan/datasets_and_models/raw/main/ratings.csv')"
      ]
    },
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              "   book_id  user_id  rating\n",
              "0        1      314       5\n",
              "1        1      439       3\n",
              "2        1      588       5\n",
              "3        1     1169       4\n",
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      ],
      "source": [
        "dataset.head()"
      ]
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            "text/plain": [
              "(981756, 3)"
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      "source": [
        "dataset.shape"
      ]
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      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "stmHEJQtipCL"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(dataset, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
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              "        book_id  user_id  rating\n",
              "341848     3423     4608       2\n",
              "964349     9811    36373       5\n",
              "645459     6485     2957       4\n",
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              "        document.querySelector('#df-bdd9ead4-efbf-41ab-a9f7-8cad5d4d65c9 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-bdd9ead4-efbf-41ab-a9f7-8cad5d4d65c9');\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-cb2bac86-6e59-40c2-85eb-5a2d07f1fed8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cb2bac86-6e59-40c2-85eb-5a2d07f1fed8')\"\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-cb2bac86-6e59-40c2-85eb-5a2d07f1fed8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RqACdkKNipCN",
        "outputId": "d1f08790-c993-4feb-d20c-f9d2fac913e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        book_id  user_id  rating\n",
              "646451     6495    19643       5\n",
              "614851     6175     8563       4\n",
              "974393     9920    52110       3\n",
              "21471       215    33864       5\n",
              "272540     2728    16587       3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29036db8-ba4a-4434-9efe-96a7eaf6d577\" 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>book_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>646451</th>\n",
              "      <td>6495</td>\n",
              "      <td>19643</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614851</th>\n",
              "      <td>6175</td>\n",
              "      <td>8563</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>974393</th>\n",
              "      <td>9920</td>\n",
              "      <td>52110</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21471</th>\n",
              "      <td>215</td>\n",
              "      <td>33864</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>272540</th>\n",
              "      <td>2728</td>\n",
              "      <td>16587</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</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-29036db8-ba4a-4434-9efe-96a7eaf6d577')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29036db8-ba4a-4434-9efe-96a7eaf6d577 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-29036db8-ba4a-4434-9efe-96a7eaf6d577');\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-2c526cef-0063-42ca-9e2d-7545d508a916\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2c526cef-0063-42ca-9e2d-7545d508a916')\"\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-2c526cef-0063-42ca-9e2d-7545d508a916 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSs2w7l7ipCN",
        "outputId": "f2932549-5158-4b8a-d33e-719a3eb0646f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53424"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "n_users = len(dataset.user_id.unique())\n",
        "n_users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgBwSpqyipCN",
        "outputId": "5f498dca-17a5-4c6b-aeb7-12dca19e0994"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "n_books = len(dataset.book_id.unique())\n",
        "n_books"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6TrxD_wipCP"
      },
      "source": [
        "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
        "مدلی که خواهیم داشت از اجزای اصلی زیر تشکیل خواهد شد</div>\n",
        "\n",
        "    -Input: Input for both books and users\n",
        "    -Embedding Layers: Embeddings for books and users\n",
        "    -Dot: combines embeddings using a dot product"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "NCAkend2ipCP"
      },
      "outputs": [],
      "source": [
        "book_input = Input(shape=[1], name=\"Book-Input\")\n",
        "book_embedding = Embedding(n_books+1, 5, name=\"Book-Embedding\")(book_input)\n",
        "book_vec = Flatten(name=\"Flatten-Books\")(book_embedding)\n",
        "\n",
        "user_input = Input(shape=[1], name=\"User-Input\")\n",
        "user_embedding = Embedding(n_users+1, 5, name=\"User-Embedding\")(user_input)\n",
        "user_vec = Flatten(name=\"Flatten-Users\")(user_embedding)\n",
        "\n",
        "prod = Dot(name=\"Dot-Product\", axes=1)([book_vec, user_vec])\n",
        "model = Model([user_input, book_input], prod)\n",
        "model.compile('adam', 'mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N4h5Qv_ipCQ",
        "outputId": "5f9d1110-cdf3-410c-ff0a-cf966b41d130"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " Book-Input (InputLayer)     [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " User-Input (InputLayer)     [(None, 1)]                  0         []                            \n",
            "                                                                                                  \n",
            " Book-Embedding (Embedding)  (None, 1, 5)                 50005     ['Book-Input[0][0]']          \n",
            "                                                                                                  \n",
            " User-Embedding (Embedding)  (None, 1, 5)                 267125    ['User-Input[0][0]']          \n",
            "                                                                                                  \n",
            " Flatten-Books (Flatten)     (None, 5)                    0         ['Book-Embedding[0][0]']      \n",
            "                                                                                                  \n",
            " Flatten-Users (Flatten)     (None, 5)                    0         ['User-Embedding[0][0]']      \n",
            "                                                                                                  \n",
            " Dot-Product (Dot)           (None, 1)                    0         ['Flatten-Books[0][0]',       \n",
            "                                                                     'Flatten-Users[0][0]']       \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 317130 (1.21 MB)\n",
            "Trainable params: 317130 (1.21 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate([test.user_id, test.book_id], test.rating)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOA7tLqTlS7m",
        "outputId": "0203ff5b-bf99-4dd7-950c-4a7aa7c97e57"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6136/6136 [==============================] - 14s 2ms/step - loss: 15.8283\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.828289031982422"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict([test.user_id.head(10), test.book_id.head(10)])\n",
        "\n",
        "for i in range(0,10):\n",
        "   print(predictions[i], test.rating.iloc[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VvGyk8ilkxs",
        "outputId": "add1aa5b-c524-4eb5-acd7-ddacb73e92af"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 57ms/step\n",
            "[2.3068336e-05] 5\n",
            "[-0.00092155] 4\n",
            "[-0.00312227] 3\n",
            "[-8.4236366e-05] 5\n",
            "[0.00050229] 3\n",
            "[0.00033401] 3\n",
            "[-0.00278908] 3\n",
            "[-1.9127208e-05] 4\n",
            "[-0.00223821] 3\n",
            "[0.00169448] 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "4PsieaanipCQ",
        "outputId": "fcbf56cd-7bc0-45c6-936b-d6fa382a1217"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "24544/24544 [==============================] - 104s 4ms/step - loss: 13.6461\n",
            "Epoch 2/10\n",
            "24544/24544 [==============================] - 101s 4ms/step - loss: 4.5767\n",
            "Epoch 3/10\n",
            "24544/24544 [==============================] - 101s 4ms/step - loss: 1.8401\n",
            "Epoch 4/10\n",
            "24544/24544 [==============================] - 102s 4ms/step - loss: 1.2208\n",
            "Epoch 5/10\n",
            "24544/24544 [==============================] - 102s 4ms/step - loss: 0.9730\n",
            "Epoch 6/10\n",
            "24544/24544 [==============================] - 103s 4ms/step - loss: 0.8442\n",
            "Epoch 7/10\n",
            "24544/24544 [==============================] - 102s 4ms/step - loss: 0.7690\n",
            "Epoch 8/10\n",
            "24544/24544 [==============================] - 103s 4ms/step - loss: 0.7218\n",
            "Epoch 9/10\n",
            "24544/24544 [==============================] - 102s 4ms/step - loss: 0.6899\n",
            "Epoch 10/10\n",
            "24544/24544 [==============================] - 102s 4ms/step - loss: 0.6674\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from keras.models import load_model\n",
        "import os\n",
        "\n",
        "if os.path.exists('regression_model.h5'):\n",
        "    model = load_model('regression_model.h5')\n",
        "else:\n",
        "    history = model.fit([train.user_id, train.book_id], train.rating, epochs=10, verbose=1)\n",
        "    model.save('regression_model.h5')\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.xlabel(\"Epochs\")\n",
        "    plt.ylabel(\"Training Error\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VP-s28ifipCQ",
        "outputId": "e2765790-549b-4c9d-f217-a425d5ae9e9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6136/6136 [==============================] - 16s 3ms/step - loss: 0.9603\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9602550268173218"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "model.evaluate([test.user_id, test.book_id], test.rating)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tXnnBQpipCQ",
        "outputId": "8b839169-c370-4028-a8f3-b7b4e96709ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "[5.2514153] 5\n",
            "[3.7159991] 4\n",
            "[3.7052584] 3\n",
            "[3.8982012] 5\n",
            "[3.5516946] 3\n",
            "[3.5172405] 3\n",
            "[2.9716544] 3\n",
            "[4.7415504] 4\n",
            "[4.4628468] 3\n",
            "[3.9871783] 5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "predictions = model.predict([test.user_id.head(10), test.book_id.head(10)])\n",
        "\n",
        "[print(predictions[i], test.rating.iloc[i]) for i in range(0,10)]"
      ]
    },
    {
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