rs_matrix_factorization.ipynb

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      "name": "rs_matrix_factorization.ipynb",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/alexislintw/Movie-Recommender-Systems/blob/main/rs_matrix_factorization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA2JmZDOXDt1"
      },
      "source": [
        "# Movie Recommender System\n",
        "### Base on Matrix Factorization\n",
        "- Author: Alexis\n",
        "- Updated: 2022/2/27\n",
        "- Updated: 2021/2/15\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VzhTqQ-Pq_C"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfSxVxHLRUN6"
      },
      "source": [
        "### Load and organize the data"
      ]
    },
    {
      "cell_type": "code",
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        "id": "5WoIsHgtQCKB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40270213-e30d-4294-a5f2-06b36e492d08"
      },
      "source": [
        "# Download and unzip dataset\n",
        "\n",
        "!wget http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
        "!unzip ml-latest-small.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-27 13:36:24--  http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\n",
            "Resolving files.grouplens.org (files.grouplens.org)... 128.101.65.152\n",
            "Connecting to files.grouplens.org (files.grouplens.org)|128.101.65.152|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 978202 (955K) [application/zip]\n",
            "Saving to: ‘ml-latest-small.zip’\n",
            "\n",
            "ml-latest-small.zip 100%[===================>] 955.28K  4.56MB/s    in 0.2s    \n",
            "\n",
            "2022-02-27 13:36:25 (4.56 MB/s) - ‘ml-latest-small.zip’ saved [978202/978202]\n",
            "\n",
            "Archive:  ml-latest-small.zip\n",
            "   creating: ml-latest-small/\n",
            "  inflating: ml-latest-small/links.csv  \n",
            "  inflating: ml-latest-small/tags.csv  \n",
            "  inflating: ml-latest-small/ratings.csv  \n",
            "  inflating: ml-latest-small/README.txt  \n",
            "  inflating: ml-latest-small/movies.csv  \n"
          ]
        }
      ]
    },
    {
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      "source": [
        "# Merge data\n",
        "\n",
        "PATH = '/content/drive/MyDrive/Dataset/ml-latest-small/'\n",
        "ratings = pd.read_csv(PATH + 'ratings.csv')\n",
        "movies = pd.read_csv(PATH + 'movies.csv')\n",
        "combine_movie_rating = pd.merge(ratings,movies,on='movieId')\n",
        "\n",
        "print(combine_movie_rating.shape)\n",
        "combine_movie_rating.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100836, 6)\n"
          ]
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              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
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              "                                                     [key], {});\n",
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            ],
            "text/plain": [
              "   userId  ...                                       genres\n",
              "0       1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1       5  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "2       7  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "3      15  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "4      17  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
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        "id": "lGjKmjNwRBD6",
        "outputId": "c0157d08-1a67-4639-9683-389ce199d9b4"
      },
      "source": [
        "# Calculate the number of ratings by movie\n",
        "\n",
        "movie_rating_count = (combine_movie_rating.\n",
        "                  groupby(by=['title'])['rating'].\n",
        "                  count().\n",
        "                  reset_index().\n",
        "                  rename(columns={'rating':'totalRatingCount'})\n",
        "                  [['title','totalRatingCount']]\n",
        "                 )\n",
        "\n",
        "print(movie_rating_count.shape)\n",
        "movie_rating_count.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9719, 2)\n"
          ]
        },
        {
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>totalRatingCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>'71 (2014)</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>'Hellboy': The Seeds of Creation (2004)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>'Round Midnight (1986)</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>'Salem's Lot (2004)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>'Til There Was You (1997)</td>\n",
              "      <td>2</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "                                     title  totalRatingCount\n",
              "0                               '71 (2014)                 1\n",
              "1  'Hellboy': The Seeds of Creation (2004)                 1\n",
              "2                   'Round Midnight (1986)                 2\n",
              "3                      'Salem's Lot (2004)                 1\n",
              "4                'Til There Was You (1997)                 2"
            ]
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      "source": [
        "# Merge data\n",
        "\n",
        "rating_with_total_rating_count = combine_movie_rating.merge(movie_rating_count,\n",
        "                                                            left_on='title',\n",
        "                                                            right_on='title',\n",
        "                                                            how=\"inner\")\n",
        "\n",
        "print(rating_with_total_rating_count.shape)\n",
        "rating_with_total_rating_count.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100836, 7)\n"
          ]
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>totalRatingCount</th>\n",
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              "  </thead>\n",
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              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>847434962</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1106635946</td>\n",
              "      <td>Toy Story (1995)</td>\n",
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              "      <td>215</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1510577970</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
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              "      <td>4.5</td>\n",
              "      <td>1305696483</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ea8203f-7e30-43b5-9ef6-bfc466cc8c21')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      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-7ea8203f-7e30-43b5-9ef6-bfc466cc8c21 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-7ea8203f-7e30-43b5-9ef6-bfc466cc8c21');\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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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   userId  ...  totalRatingCount\n",
              "0       1  ...               215\n",
              "1       5  ...               215\n",
              "2       7  ...               215\n",
              "3      15  ...               215\n",
              "4      17  ...               215\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "u-Z_ZKP9Sk-g",
        "outputId": "b3a483ca-bab2-4901-d52d-528018096b25"
      },
      "source": [
        "# Filter out movies with number of ratings below the threshold\n",
        "\n",
        "popularity_threshold = 20\n",
        "\n",
        "rating_popular_movie = rating_with_total_rating_count[rating_with_total_rating_count['totalRatingCount']>=popularity_threshold]\n",
        "\n",
        "print(rating_popular_movie.shape)\n",
        "rating_popular_movie.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(67901, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-da894581-846c-4cc1-b707-30f4fe00a91e\">\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>totalRatingCount</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1305696483</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da894581-846c-4cc1-b707-30f4fe00a91e')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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",
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              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\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-da894581-846c-4cc1-b707-30f4fe00a91e 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-da894581-846c-4cc1-b707-30f4fe00a91e');\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": [
              "   userId  ...  totalRatingCount\n",
              "0       1  ...               215\n",
              "1       5  ...               215\n",
              "2       7  ...               215\n",
              "3      15  ...               215\n",
              "4      17  ...               215\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08Fu-X-LULXD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "7db46023-945c-422e-c18a-db0c235f0583"
      },
      "source": [
        "# Filter out duplicate data\n",
        "\n",
        "rating_popular_movie = rating_popular_movie.drop_duplicates(['userId','title'])\n",
        "\n",
        "print(rating_popular_movie.shape)\n",
        "rating_popular_movie.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(67898, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-f157b2e6-58c5-44c1-9526-d16140914964\">\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>title</th>\n",
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              "      <td>215</td>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      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: #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-f157b2e6-58c5-44c1-9526-d16140914964 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-f157b2e6-58c5-44c1-9526-d16140914964');\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": [
              "   userId  ...  totalRatingCount\n",
              "0       1  ...               215\n",
              "1       5  ...               215\n",
              "2       7  ...               215\n",
              "3      15  ...               215\n",
              "4      17  ...               215\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "f9UzJbgCStF3",
        "outputId": "08f9824f-df8a-4118-e1ab-09d2d879ee2c"
      },
      "source": [
        "# Convert to user-item matrix\n",
        "\n",
        "rating_pivot = rating_popular_movie.pivot(index=\"userId\",columns=\"title\",values=\"rating\").fillna(0)\n",
        "\n",
        "print(rating_pivot.shape)\n",
        "rating_pivot.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(610, 1297)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>title</th>\n",
              "      <th>(500) Days of Summer (2009)</th>\n",
              "      <th>10 Things I Hate About You (1999)</th>\n",
              "      <th>101 Dalmatians (1996)</th>\n",
              "      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n",
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              "      <th>27 Dresses (2008)</th>\n",
              "      <th>28 Days (2000)</th>\n",
              "      <th>28 Days Later (2002)</th>\n",
              "      <th>28 Weeks Later (2007)</th>\n",
              "      <th>300 (2007)</th>\n",
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              "      <th>A.I. Artificial Intelligence (2001)</th>\n",
              "      <th>About Schmidt (2002)</th>\n",
              "      <th>About a Boy (2002)</th>\n",
              "      <th>Abyss, The (1989)</th>\n",
              "      <th>Ace Ventura: Pet Detective (1994)</th>\n",
              "      <th>Ace Ventura: When Nature Calls (1995)</th>\n",
              "      <th>Adaptation (2002)</th>\n",
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              "      <th>Airheads (1994)</th>\n",
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              "      <th>Whiplash (2014)</th>\n",
              "      <th>White Men Can't Jump (1992)</th>\n",
              "      <th>Who Framed Roger Rabbit? (1988)</th>\n",
              "      <th>Whole Nine Yards, The (2000)</th>\n",
              "      <th>Wild Things (1998)</th>\n",
              "      <th>Wild Wild West (1999)</th>\n",
              "      <th>William Shakespeare's Romeo + Juliet (1996)</th>\n",
              "      <th>Willow (1988)</th>\n",
              "      <th>Willy Wonka &amp; the Chocolate Factory (1971)</th>\n",
              "      <th>Witness (1985)</th>\n",
              "      <th>Wizard of Oz, The (1939)</th>\n",
              "      <th>Wolf of Wall Street, The (2013)</th>\n",
              "      <th>Wonder Boys (2000)</th>\n",
              "      <th>Working Girl (1988)</th>\n",
              "      <th>World Is Not Enough, The (1999)</th>\n",
              "      <th>Wreck-It Ralph (2012)</th>\n",
              "      <th>Wrestler, The (2008)</th>\n",
              "      <th>Wyatt Earp (1994)</th>\n",
              "      <th>X-Files: Fight the Future, The (1998)</th>\n",
              "      <th>X-Men (2000)</th>\n",
              "      <th>X-Men Origins: Wolverine (2009)</th>\n",
              "      <th>X-Men: Days of Future Past (2014)</th>\n",
              "      <th>X-Men: First Class (2011)</th>\n",
              "      <th>X-Men: The Last Stand (2006)</th>\n",
              "      <th>X2: X-Men United (2003)</th>\n",
              "      <th>Yes Man (2008)</th>\n",
              "      <th>You've Got Mail (1998)</th>\n",
              "      <th>Young Frankenstein (1974)</th>\n",
              "      <th>Young Guns (1988)</th>\n",
              "      <th>Zack and Miri Make a Porno (2008)</th>\n",
              "      <th>Zodiac (2007)</th>\n",
              "      <th>Zombieland (2009)</th>\n",
              "      <th>Zoolander (2001)</th>\n",
              "      <th>Zootopia (2016)</th>\n",
              "      <th>eXistenZ (1999)</th>\n",
              "      <th>xXx (2002)</th>\n",
              "      <th>¡Three Amigos! (1986)</th>\n",
              "    </tr>\n",
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              "      <th>userId</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th>1</th>\n",
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              "      <td>5.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1297 columns</p>\n",
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            ],
            "text/plain": [
              "title   (500) Days of Summer (2009)  ...  ¡Three Amigos! (1986)\n",
              "userId                               ...                       \n",
              "1                               0.0  ...                    4.0\n",
              "2                               0.0  ...                    0.0\n",
              "3                               0.0  ...                    0.0\n",
              "4                               0.0  ...                    0.0\n",
              "5                               0.0  ...                    0.0\n",
              "\n",
              "[5 rows x 1297 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLBPK-3LsnGC"
      },
      "source": [
        "### Query by user"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvGm8K2v55tD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ecff86b-bde7-43f2-ac2e-9d122dfb9a13"
      },
      "source": [
        "# Find a movie\n",
        "x = movies[movies['title'].str.contains(\"Moneyball\")]\n",
        "print(x)\n",
        "\n",
        "# Get movie titles\n",
        "movie_title = rating_pivot.columns\n",
        "movie_list = list(movie_title)\n",
        "\n",
        "#idx = movie_list.index('Moneyball (2011)')\n",
        "#print(idx)\n",
        "\n",
        "# Get the rating of a movie\n",
        "y = ratings[ratings['movieId']==89492].sort_values(by=['rating'],ascending=False)\n",
        "print(y)\n",
        "# 408,495"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      movieId             title genres\n",
            "7688    89492  Moneyball (2011)  Drama\n",
            "       userId  movieId  rating   timestamp\n",
            "61679     408    89492     5.0  1468147077\n",
            "79709     495    89492     5.0  1458635252\n",
            "64841     414    89492     4.5  1328313081\n",
            "11726      70    89492     4.5  1355185210\n",
            "83402     528    89492     4.5  1391736722\n",
            "52302     339    89492     4.5  1460346554\n",
            "11554      68    89492     4.5  1321936093\n",
            "81420     514    89492     4.5  1533873024\n",
            "46463     305    89492     4.0  1460303444\n",
            "53186     351    89492     4.0  1326027883\n",
            "79225     490    89492     4.0  1328145178\n",
            "9029       62    89492     4.0  1521489573\n",
            "69978     448    89492     4.0  1355584097\n",
            "37142     249    89492     4.0  1346758113\n",
            "25738     177    89492     4.0  1435526646\n",
            "22418     152    89492     4.0  1450867862\n",
            "15908     103    89492     4.0  1431955333\n",
            "11949      73    89492     4.0  1464282937\n",
            "82390     522    89492     4.0  1325653283\n",
            "49683     318    89492     3.5  1424797380\n",
            "16742     105    89492     3.5  1446572244\n",
            "94833     599    89492     3.5  1498542405\n",
            "48954     317    89492     3.0  1430513469\n",
            "43873     292    89492     3.0  1326661225\n",
            "45316     298    89492     2.5  1449589078\n",
            "50600     326    89492     1.0  1436898666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOrput1PqvAY",
        "outputId": "a0d4128a-922c-4e93-e665-37a8065aeba8"
      },
      "source": [
        "### Matrix factorization\n",
        "\n",
        "import numpy as np\n",
        "from sklearn.utils.extmath import randomized_svd\n",
        "\n",
        "A = np.array(rating_pivot)\n",
        "u, s, vt = randomized_svd(A, n_components = 12)\n",
        "print(u.shape)\n",
        "print(s.shape)\n",
        "print(vt.shape)\n",
        "\n",
        "# New rating matrix\n",
        "nR = np.dot(u,vt)\n",
        "print(nR.shape)\n",
        "\n",
        "print(type(nR))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(610, 12)\n",
            "(12,)\n",
            "(12, 1297)\n",
            "(610, 1297)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/extmath.py:376: FutureWarning: If 'random_state' is not supplied, the current default is to use 0 as a fixed seed. This will change to  None in version 1.2 leading to non-deterministic results that better reflect nature of the randomized_svd solver. If you want to silence this warning, set 'random_state' to an integer seed or to None explicitly depending if you want your code to be deterministic or not.\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fl7sD8eAEcyX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c85b272-2758-42aa-bf76-dd41b66aa975"
      },
      "source": [
        "# Get all new ratings for a user\n",
        "user_idx = 494\n",
        "scores = nR[user_idx]\n",
        "\n",
        "# Get the tuple of score and index \n",
        "scores = list(enumerate(scores))\n",
        "\n",
        "# Sort by rating\n",
        "scores = sorted(scores,key=lambda x:x[1],reverse=True)\n",
        "\n",
        "# Get the top 10\n",
        "scores = scores[:10]\n",
        "\n",
        "# Get the movie indices\n",
        "movie_indices = [i[0] for i in scores]\n",
        "\n",
        "# Convert to the movie titles\n",
        "recomm = [movie_list[idx] for idx in movie_indices]\n",
        "\n",
        "# Display the top 10 movies\n",
        "for r in recomm:\n",
        "    print(r)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Goodfellas (1990)\n",
            "Hangover, The (2009)\n",
            "Departed, The (2006)\n",
            "Godfather, The (1972)\n",
            "Social Network, The (2010)\n",
            "Inglourious Basterds (2009)\n",
            "Wolf of Wall Street, The (2013)\n",
            "Inception (2010)\n",
            "Fight Club (1999)\n",
            "American History X (1998)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STvNRZLftIYo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}