Content_Based_movies.ipynb

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      "name": "ML0101EN_RecSys_Content_Based_movies_py_v1.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/RohitIrvisetty/Recommender-Systems/blob/main/Content_Based_movies.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": "0hrkRRiCVe4t"
      },
      "source": [
        "\n",
        "\n",
        "<h1 align=\"center\"><font size=\"5\">CONTENT-BASED RECOMMENDATION SYSTEMS</font></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2yEsRENVfBU"
      },
      "source": [
        "Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, I will implement Content-based recommendation systems and implement a simple version of one using Python and the Pandas library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JtN_CVJ2VfBV"
      },
      "source": [
        "<a id=\"ref1\"></a>\n",
        "# Acquiring the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lCH73tyVfBW"
      },
      "source": [
        "\n"
      ]
    },
    {
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      "metadata": {
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        },
        "id": "FXq_84r1VfBY",
        "outputId": "9edeaf84-4361-47b3-e3cf-8bfba83eb736"
      },
      "source": [
        "!wget -O moviedataset.zip https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip\n",
        "print('unziping ...')\n",
        "!unzip -o -j moviedataset.zip "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-02-03 10:09:30--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/moviedataset.zip\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 160301210 (153M) [application/zip]\n",
            "Saving to: ‘moviedataset.zip’\n",
            "\n",
            "moviedataset.zip    100%[===================>] 152.88M  33.7MB/s    in 4.4s    \n",
            "\n",
            "2021-02-03 10:09:35 (34.7 MB/s) - ‘moviedataset.zip’ saved [160301210/160301210]\n",
            "\n",
            "unziping ...\n",
            "Archive:  moviedataset.zip\n",
            "  inflating: links.csv               \n",
            "  inflating: movies.csv              \n",
            "  inflating: ratings.csv             \n",
            "  inflating: README.txt              \n",
            "  inflating: tags.csv                \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1rlDr90VfBa"
      },
      "source": [
        "<a id=\"ref2\"></a>\n",
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkOgxD9iVfBb"
      },
      "source": [
        "First, let's get all of the imports out of the way:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "id": "_VI1ME-wVfBc"
      },
      "source": [
        "#Dataframe manipulation library\n",
        "import pandas as pd\n",
        "\n",
        "from math import sqrt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTSAbR0sVfBd"
      },
      "source": [
        "Now let's read each file into their Dataframes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ku9gEORyVfBe",
        "outputId": "7ca6024a-4411-4e14-c49f-5223eb27b075"
      },
      "source": [
        "#Storing the movie information into a pandas dataframe\n",
        "movies_df = pd.read_csv('movies.csv')\n",
        "#Storing the user information into a pandas dataframe\n",
        "ratings_df = pd.read_csv('ratings.csv')\n",
        "\n",
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...                                       genres\n",
              "0        1  ...  Adventure|Animation|Children|Comedy|Fantasy\n",
              "1        2  ...                   Adventure|Children|Fantasy\n",
              "2        3  ...                               Comedy|Romance\n",
              "3        4  ...                         Comedy|Drama|Romance\n",
              "4        5  ...                                       Comedy\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 204
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        "id": "2H9c_ftjVfBg",
        "outputId": "b5c8a221-ba9d-4624-caad-1fdeb9f156b1"
      },
      "source": [
        "\n",
        "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
        "#Removing the parentheses\n",
        "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
        "#Removing the years from the 'title' column\n",
        "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
        "#Applying the strip function to get rid of any ending whitespace characters that may have appeared\n",
        "movies_df['title'] = movies_df['title'].apply(lambda x: x.strip())\n",
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
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              "      <th>year</th>\n",
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              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>Adventure|Children|Fantasy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>Comedy|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Comedy|Drama|Romance</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "3        4  ...  1995\n",
              "4        5  ...  1995\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
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    },
    {
      "cell_type": "code",
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        "colab": {
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        },
        "id": "X3w7MLEkVfBi",
        "outputId": "a9135fc5-316d-40b0-fe0d-fee76e46e166"
      },
      "source": [
        "#Every genre is separated by a | so we simply have to call the split function on |\n",
        "movies_df['genres'] = movies_df.genres.str.split('|')\n",
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>year</th>\n",
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              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "3        4  ...  1995\n",
              "4        5  ...  1995\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 6
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SickNwbMVfBj"
      },
      "source": [
        "Since keeping genres in a list format isn't optimal for the content-based recommendation system technique, we will use the One Hot Encoding technique to convert the list of genres to a vector where each column corresponds to one possible value of the feature. This encoding is needed for feeding categorical data. In this case, we store every different genre in columns that contain either 1 or 0. 1 shows that a movie has that genre and 0 shows that it doesn't. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "d9e099a1-5a4c-44f8-ba2d-16a94cddad6f"
      },
      "source": [
        "\n",
        "moviesWithGenres_df = movies_df.copy()\n",
        "\n",
        "#For every row in the dataframe, iterate through the list of genres and place a 1 into the corresponding column\n",
        "for index, row in movies_df.iterrows():\n",
        "    for genre in row['genres']:\n",
        "        moviesWithGenres_df.at[index, genre] = 1\n",
        "\n",
        "moviesWithGenres_df = moviesWithGenres_df.fillna(0)\n",
        "moviesWithGenres_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, Romance]</td>\n",
              "      <td>1995</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId                        title  ... Film-Noir (no genres listed)\n",
              "0        1                    Toy Story  ...       0.0                0.0\n",
              "1        2                      Jumanji  ...       0.0                0.0\n",
              "2        3             Grumpier Old Men  ...       0.0                0.0\n",
              "3        4            Waiting to Exhale  ...       0.0                0.0\n",
              "4        5  Father of the Bride Part II  ...       0.0                0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3Uj_RazVfBm"
      },
      "source": [
        "Next, let's look at the ratings dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "V3Bv6BaDVfBn",
        "outputId": "43361ec2-396d-41ad-fe0b-5f7c72570a69"
      },
      "source": [
        "ratings_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>169</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1204927694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2471</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1204927438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>48516</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1204927435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2571</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1436165433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>109487</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1436165496</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1      169     2.5  1204927694\n",
              "1       1     2471     3.0  1204927438\n",
              "2       1    48516     5.0  1204927435\n",
              "3       2     2571     3.5  1436165433\n",
              "4       2   109487     4.0  1436165496"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vy5xR8ieVfBo",
        "outputId": "a690a606-0cc4-45b6-93f1-d8bcf9dd8981"
      },
      "source": [
        "#Drop removes a specified row or column from a dataframe\n",
        "ratings_df = ratings_df.drop('timestamp', 1)\n",
        "ratings_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\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",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>169</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2471</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>48516</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2571</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>109487</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating\n",
              "0       1      169     2.5\n",
              "1       1     2471     3.0\n",
              "2       1    48516     5.0\n",
              "3       2     2571     3.5\n",
              "4       2   109487     4.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFhdqnmpVfBp"
      },
      "source": [
        "<a id=\"ref3\"></a>\n",
        "# Content-Based recommendation system"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JSErXsnyVfBp"
      },
      "source": [
        "This technique attempts to figure out what a user's favourite aspects of an item is, and then recommends items that present those aspects. In our case, we're going to try to figure out the input's favorite genres from the movies and ratings given.\n",
        "\n",
        "Let's begin by creating an input user to recommend movies to:\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "7yK-DIoiVfBq",
        "outputId": "305738fe-3f8e-47b4-b362-5ec1194001c0"
      },
      "source": [
        "userInput = [\n",
        "            {'title':'Breakfast Club, The', 'rating':5},\n",
        "            {'title':'Toy Story', 'rating':3.5},\n",
        "            {'title':'Jumanji', 'rating':2},\n",
        "            {'title':\"Pulp Fiction\", 'rating':5},\n",
        "            {'title':'Akira', 'rating':4.5}\n",
        "         ] \n",
        "inputMovies = pd.DataFrame(userInput)\n",
        "inputMovies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        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>title</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Breakfast Club, The</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Toy Story</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jumanji</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Akira</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 title  rating\n",
              "0  Breakfast Club, The     5.0\n",
              "1            Toy Story     3.5\n",
              "2              Jumanji     2.0\n",
              "3         Pulp Fiction     5.0\n",
              "4                Akira     4.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4AJgo4FVfBs"
      },
      "source": [
        "#### Add movieId to input user\n",
        "With the input complete, let's extract the input movie's ID's from the movies dataframe and add them into it.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nFsaTTfIVfBt",
        "outputId": "670a8703-092f-46c5-ab27-c2fb85d08c9a"
      },
      "source": [
        "#Filtering out the movies by title\n",
        "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
        "#Then merging it so we can get the movieId. It's implicitly merging it by title.\n",
        "inputMovies = pd.merge(inputId, inputMovies)\n",
        "#Dropping information we won't use from the input dataframe\n",
        "inputMovies = inputMovies.drop('genres', 1).drop('year', 1)\n",
        "#Final input dataframe\n",
        "#If a movie you added in above isn't here, then it might not be in the original \n",
        "#dataframe or it might spelled differently, please check capitalisation.\n",
        "inputMovies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>296</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1274</td>\n",
              "      <td>Akira</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1968</td>\n",
              "      <td>Breakfast Club, The</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId                title  rating\n",
              "0        1            Toy Story     3.5\n",
              "1        2              Jumanji     2.0\n",
              "2      296         Pulp Fiction     5.0\n",
              "3     1274                Akira     4.5\n",
              "4     1968  Breakfast Club, The     5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "Waur3QjCVfBv",
        "outputId": "7398bf52-7ca9-4b8f-b7db-bd938aa74d97"
      },
      "source": [
        "#Filtering out the movies from the input\n",
        "userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
        "userMovies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Adventure, Children, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>293</th>\n",
              "      <td>296</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>[Comedy, Crime, Drama, Thriller]</td>\n",
              "      <td>1994</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>1246</th>\n",
              "      <td>1274</td>\n",
              "      <td>Akira</td>\n",
              "      <td>[Action, Adventure, Animation, Sci-Fi]</td>\n",
              "      <td>1988</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1885</th>\n",
              "      <td>1968</td>\n",
              "      <td>Breakfast Club, The</td>\n",
              "      <td>[Comedy, Drama]</td>\n",
              "      <td>1985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      movieId                title  ... Film-Noir (no genres listed)\n",
              "0           1            Toy Story  ...       0.0                0.0\n",
              "1           2              Jumanji  ...       0.0                0.0\n",
              "293       296         Pulp Fiction  ...       0.0                0.0\n",
              "1246     1274                Akira  ...       0.0                0.0\n",
              "1885     1968  Breakfast Club, The  ...       0.0                0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "Lk5NRNxLVfBw",
        "outputId": "4af5b3e8-2b9f-4316-f5b2-a4b858bea1b0"
      },
      "source": [
        "#Resetting the index to avoid future issues\n",
        "userMovies = userMovies.reset_index(drop=True)\n",
        "#Dropping unnecessary issues due to save memory and to avoid issues\n",
        "userGenreTable = userMovies.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
        "userGenreTable"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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",
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              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Adventure  Animation  Children  ...  Western  Film-Noir  (no genres listed)\n",
              "0        1.0        1.0       1.0  ...      0.0        0.0                 0.0\n",
              "1        1.0        0.0       1.0  ...      0.0        0.0                 0.0\n",
              "2        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "3        1.0        1.0       0.0  ...      0.0        0.0                 0.0\n",
              "4        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A0e8yYRTVfBx"
      },
      "source": [
        "Now we're ready to start learning the input's preferences!\n",
        "\n",
        "To do this, we're going to turn each genre into weights. We can do this by using the input's reviews and multiplying them into the input's genre table and then summing up the resulting table by column. This operation is actually a dot product between a matrix and a vector, so we can simply accomplish by calling Pandas's \"dot\" function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlOImeyLVfBy",
        "outputId": "fd506dac-974d-4a04-bd2f-d360b60463d9"
      },
      "source": [
        "inputMovies['rating']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3.5\n",
              "1    2.0\n",
              "2    5.0\n",
              "3    4.5\n",
              "4    5.0\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZA-23wfkVfBz",
        "outputId": "1695a44c-1521-4da9-d594-b32911fb1881"
      },
      "source": [
        "#Dot produt to get weights\n",
        "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
        "#The user profile\n",
        "userProfile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adventure             10.0\n",
              "Animation              8.0\n",
              "Children               5.5\n",
              "Comedy                13.5\n",
              "Fantasy                5.5\n",
              "Romance                0.0\n",
              "Drama                 10.0\n",
              "Action                 4.5\n",
              "Crime                  5.0\n",
              "Thriller               5.0\n",
              "Horror                 0.0\n",
              "Mystery                0.0\n",
              "Sci-Fi                 4.5\n",
              "IMAX                   0.0\n",
              "Documentary            0.0\n",
              "War                    0.0\n",
              "Musical                0.0\n",
              "Western                0.0\n",
              "Film-Noir              0.0\n",
              "(no genres listed)     0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y34-MHbIVfB4"
      },
      "source": [
        "Now, we have the weights for every of the user's preferences. This is known as the User Profile. Using this, we can recommend movies that satisfy the user's preferences."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C0nXWgltVfB5"
      },
      "source": [
        "Let's start by extracting the genre table from the original dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "x3XlrWXlVfB7",
        "outputId": "ad01e6a6-5d2b-42b1-db8a-bd29054d6430"
      },
      "source": [
        "#Now let's get the genres of every movie in our original dataframe\n",
        "genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId'])\n",
        "#And drop the unnecessary information\n",
        "genreTable = genreTable.drop('movieId', 1).drop('title', 1).drop('genres', 1).drop('year', 1)\n",
        "genreTable.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
              "      <th>Comedy</th>\n",
              "      <th>Fantasy</th>\n",
              "      <th>Romance</th>\n",
              "      <th>Drama</th>\n",
              "      <th>Action</th>\n",
              "      <th>Crime</th>\n",
              "      <th>Thriller</th>\n",
              "      <th>Horror</th>\n",
              "      <th>Mystery</th>\n",
              "      <th>Sci-Fi</th>\n",
              "      <th>IMAX</th>\n",
              "      <th>Documentary</th>\n",
              "      <th>War</th>\n",
              "      <th>Musical</th>\n",
              "      <th>Western</th>\n",
              "      <th>Film-Noir</th>\n",
              "      <th>(no genres listed)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.0</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Adventure  Animation  Children  ...  Western  Film-Noir  (no genres listed)\n",
              "movieId                                  ...                                        \n",
              "1              1.0        1.0       1.0  ...      0.0        0.0                 0.0\n",
              "2              1.0        0.0       1.0  ...      0.0        0.0                 0.0\n",
              "3              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "4              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "5              0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Czr6Asd7VfB7",
        "outputId": "bddfb0fc-ab30-421a-9af6-0e888d3e9ae0"
      },
      "source": [
        "genreTable.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34208, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reWARJoKVfB8"
      },
      "source": [
        "With the input's profile and the complete list of movies and their genres in hand, we're going to take the weighted average of every movie based on the input profile and recommend the top twenty movies that most satisfy it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd77UdRYVfB8",
        "outputId": "b129681b-e6f5-479a-da47-4ad3e296b11f"
      },
      "source": [
        "#Multiply the genres by the weights and then take the weighted average\n",
        "recommendationTable_df = ((genreTable*userProfile).sum(axis=1))/(userProfile.sum())\n",
        "recommendationTable_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "1    0.594406\n",
              "2    0.293706\n",
              "3    0.188811\n",
              "4    0.328671\n",
              "5    0.188811\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0MNN7DJVfB9",
        "outputId": "152ec83b-71b2-4b10-b36b-5680d320b21c"
      },
      "source": [
        "recommendationTable_df = recommendationTable_df.sort_values(ascending=False)\n",
        "recommendationTable_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "5018      0.748252\n",
              "26093     0.734266\n",
              "27344     0.720280\n",
              "148775    0.685315\n",
              "6902      0.678322\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1w0ALWaVfB-"
      },
      "source": [
        "Now here's the recommendation table!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "jupyter": {
          "outputs_hidden": false
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "bhWXushtVfB-",
        "outputId": "ffd7efdd-03ff-4db6-d082-81cc18f130c1"
      },
      "source": [
        "#The final recommendation table\n",
        "movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>movieId</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>664</th>\n",
              "      <td>673</td>\n",
              "      <td>Space Jam</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fanta...</td>\n",
              "      <td>1996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1824</th>\n",
              "      <td>1907</td>\n",
              "      <td>Mulan</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Drama...</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2902</th>\n",
              "      <td>2987</td>\n",
              "      <td>Who Framed Roger Rabbit?</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Crime...</td>\n",
              "      <td>1988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4923</th>\n",
              "      <td>5018</td>\n",
              "      <td>Motorama</td>\n",
              "      <td>[Adventure, Comedy, Crime, Drama, Fantasy, Mys...</td>\n",
              "      <td>1991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6793</th>\n",
              "      <td>6902</td>\n",
              "      <td>Interstate 60</td>\n",
              "      <td>[Adventure, Comedy, Drama, Fantasy, Mystery, S...</td>\n",
              "      <td>2002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8605</th>\n",
              "      <td>26093</td>\n",
              "      <td>Wonderful World of the Brothers Grimm, The</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Drama...</td>\n",
              "      <td>1962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8783</th>\n",
              "      <td>26340</td>\n",
              "      <td>Twelve Tasks of Asterix, The (Les douze travau...</td>\n",
              "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
              "      <td>1976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9296</th>\n",
              "      <td>27344</td>\n",
              "      <td>Revolutionary Girl Utena: Adolescence of Utena...</td>\n",
              "      <td>[Action, Adventure, Animation, Comedy, Drama, ...</td>\n",
              "      <td>1999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9825</th>\n",
              "      <td>32031</td>\n",
              "      <td>Robots</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Fanta...</td>\n",
              "      <td>2005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11716</th>\n",
              "      <td>51632</td>\n",
              "      <td>Atlantis: Milo's Return</td>\n",
              "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11751</th>\n",
              "      <td>51939</td>\n",
              "      <td>TMNT (Teenage Mutant Ninja Turtles)</td>\n",
              "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13250</th>\n",
              "      <td>64645</td>\n",
              "      <td>The Wrecking Crew</td>\n",
              "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
              "      <td>1968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16055</th>\n",
              "      <td>81132</td>\n",
              "      <td>Rubber</td>\n",
              "      <td>[Action, Adventure, Comedy, Crime, Drama, Film...</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18312</th>\n",
              "      <td>91335</td>\n",
              "      <td>Gruffalo, The</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Drama]</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22778</th>\n",
              "      <td>108540</td>\n",
              "      <td>Ernest &amp; Célestine (Ernest et Célestine)</td>\n",
              "      <td>[Adventure, Animation, Children, Comedy, Drama...</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22881</th>\n",
              "      <td>108932</td>\n",
              "      <td>The Lego Movie</td>\n",
              "      <td>[Action, Adventure, Animation, Children, Comed...</td>\n",
              "      <td>2014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25218</th>\n",
              "      <td>117646</td>\n",
              "      <td>Dragonheart 2: A New Beginning</td>\n",
              "      <td>[Action, Adventure, Comedy, Drama, Fantasy, Th...</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26442</th>\n",
              "      <td>122787</td>\n",
              "      <td>The 39 Steps</td>\n",
              "      <td>[Action, Adventure, Comedy, Crime, Drama, Thri...</td>\n",
              "      <td>1959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32854</th>\n",
              "      <td>146305</td>\n",
              "      <td>Princes and Princesses</td>\n",
              "      <td>[Animation, Children, Comedy, Drama, Fantasy, ...</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33509</th>\n",
              "      <td>148775</td>\n",
              "      <td>Wizards of Waverly Place: The Movie</td>\n",
              "      <td>[Adventure, Children, Comedy, Drama, Fantasy, ...</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       movieId  ...  year\n",
              "664        673  ...  1996\n",
              "1824      1907  ...  1998\n",
              "2902      2987  ...  1988\n",
              "4923      5018  ...  1991\n",
              "6793      6902  ...  2002\n",
              "8605     26093  ...  1962\n",
              "8783     26340  ...  1976\n",
              "9296     27344  ...  1999\n",
              "9825     32031  ...  2005\n",
              "11716    51632  ...  2003\n",
              "11751    51939  ...  2007\n",
              "13250    64645  ...  1968\n",
              "16055    81132  ...  2010\n",
              "18312    91335  ...  2009\n",
              "22778   108540  ...  2012\n",
              "22881   108932  ...  2014\n",
              "25218   117646  ...  2000\n",
              "26442   122787  ...  1959\n",
              "32854   146305  ...  2000\n",
              "33509   148775  ...  2009\n",
              "\n",
              "[20 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCeCPSbtVfB_"
      },
      "source": [
        "### Advantages and Disadvantages of Content-Based Filtering\n",
        "\n",
        "##### Advantages\n",
        "* Learns user's preferences\n",
        "* Highly personalized for the user\n",
        "\n",
        "##### Disadvantages\n",
        "* Doesn't take into account what others think of the item, so low quality item recommendations might happen\n",
        "* Extracting data is not always intuitive\n",
        "* Determining what characteristics of the item the user dislikes or likes is not always obvious"
      ]
    }
  ]
}