RecSys_Content_Based_movies_v1.ipynb

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      "name": "RecSys-Content-Based-movies-v1.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Machine_Learning/blob/master/RecSys_Content_Based_movies_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VIar1_26cFo8",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
        "\n",
        "<h1 align=\"center\"><font size=\"5\">CONTENT-BASED FILTERING</font></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2zAY7gscFo-",
        "colab_type": "text"
      },
      "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, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fdxect6cFpD",
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      "source": [
        "### Table of contents\n",
        "\n",
        "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
        "    <ol>\n",
        "        <li><a href=\"#ref1\">Acquiring the Data</a></li>\n",
        "        <li><a href=\"#ref2\">Preprocessing</a></li>\n",
        "        <li><a href=\"#ref3\">Content-Based Filtering</a></li>\n",
        "    </ol>\n",
        "</div>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6krmKLNcFpF",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"ref1\"></a>\n",
        "# Acquiring the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MrPFo9ncFpG",
        "colab_type": "text"
      },
      "source": [
        "To acquire and extract the data, simply run the following Bash scripts:  \n",
        "Dataset acquired from [GroupLens](http://grouplens.org/datasets/movielens/). Lets download the dataset. To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
        "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmKaxTpKcFpI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "4d39a0ac-bdc5-4ff9-f4fe-57142839e8d6"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-17 15:44:38--  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)... 67.228.254.193\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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  18.5MB/s    in 8.7s    \n",
            "\n",
            "2019-05-17 15:44:48 (17.5 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": "JXKacAaCcFpR",
        "colab_type": "text"
      },
      "source": [
        "Now you're ready to start working with the data!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Cv6eVagcFpU",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"ref2\"></a>\n",
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MhzWJYbtcFpV",
        "colab_type": "text"
      },
      "source": [
        "First, let's get all of the imports out of the way:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czTQX7CucFpX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Dataframe manipulation library\n",
        "import pandas as pd\n",
        "#Math functions, we'll only need the sqrt function so let's import only that\n",
        "from math import sqrt\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-uGtrWUlcFpe",
        "colab_type": "text"
      },
      "source": [
        "Now let's read each file into their Dataframes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wa9t4HiDcFpf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "a16e1080-3628-4b84-ef3c-8a49a8bff467"
      },
      "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",
        "#Head is a function that gets the first N rows of a dataframe. N's default is 5.\n",
        "movies_df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhlspHCGcFpn",
        "colab_type": "text"
      },
      "source": [
        "Let's also remove the year from the __title__ column by using pandas' replace function and store in a new __year__ column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "BPHyDnoFcFpp",
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          "height": 136
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        "outputId": "d5e6ae14-4835-4fe4-9b5c-fb2ec6d618b7"
      },
      "source": [
        "#Using regular expressions to find a year stored between parentheses\n",
        "#We specify the parantheses so we don't conflict with movies that have years in their titles\n",
        "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
        "movies_df.head(3)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "      <th>year</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",
              "      <td>(1995)</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",
              "      <td>(1995)</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",
              "      <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",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xGbr6-We4mp",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "689e7d23-2fb4-4b2f-82fb-2efef54e12fa"
      },
      "source": [
        "#Removing the parentheses\n",
        "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
        "movies_df.head(3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Toy Story (1995)</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 (1995)</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 (1995)</td>\n",
              "      <td>Comedy|Romance</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",
              "\n",
              "[3 rows x 4 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0ZxEar6f-n1",
        "colab_type": "code",
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          "height": 136
        },
        "outputId": "6de66e7b-5cd6-4267-a0c6-38ce7ebb09d9"
      },
      "source": [
        "#Removing the years from the 'title' column\n",
        "movies_df['title'] = movies_df.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
        "movies_df.head(3)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "      <th>year</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",
              "    </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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId  ...  year\n",
              "0        1  ...  1995\n",
              "1        2  ...  1995\n",
              "2        3  ...  1995\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stgXZ21xgbpB",
        "colab_type": "code",
        "colab": {
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          "height": 195
        },
        "outputId": "75832b8b-9f13-4aad-fd64-5f10115a61b5"
      },
      "source": [
        "#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": 15,
      "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",
              "      <th>genres</th>\n",
              "      <th>year</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",
              "    </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]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByGL0eO1cFpu",
        "colab_type": "text"
      },
      "source": [
        "With that, let's also split the values in the __Genres__ column into a __list of Genres__ to simplify future use. This can be achieved by applying Python's split string function on the correct column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "Bey8aHs0cFpv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "40b88062-6534-4611-cc66-936dadd09098"
      },
      "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": 16,
      "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>0</th>\n",
              "      <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",
              "</div>"
            ],
            "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": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7fQVlEwcFpy",
        "colab_type": "text"
      },
      "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. Let's also store this dataframe in another variable since genres won't be important for our first recommendation system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5iEheQDTcFpy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "0c09a19e-c0c9-4669-ee54-edff2ceba984"
      },
      "source": [
        "#Copying the movie dataframe into a new one since we won't need to use the genre information in our first case.\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.head(3)"
      ],
      "execution_count": 17,
      "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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId             title  ... Film-Noir (no genres listed)\n",
              "0        1         Toy Story  ...       NaN                NaN\n",
              "1        2           Jumanji  ...       NaN                NaN\n",
              "2        3  Grumpier Old Men  ...       NaN                NaN\n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPxKEFFgtxhE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "d61f60ec-0a13-466e-d7b1-21fe9efed569"
      },
      "source": [
        "#Filling in the NaN values with 0 to show that a movie doesn't have that column's genre\n",
        "moviesWithGenres_df = moviesWithGenres_df.fillna(0)\n",
        "moviesWithGenres_df.head(3)"
      ],
      "execution_count": 18,
      "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>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, 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>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",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJaY4DdRcFp1",
        "colab_type": "text"
      },
      "source": [
        "Next, let's look at the ratings dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mabs-RL_cFp2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "d80ad85e-0fef-430b-9ed9-cc3bb4cf91d6"
      },
      "source": [
        "ratings_df.head()"
      ],
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_l9nxO0cFp5",
        "colab_type": "text"
      },
      "source": [
        "Every row in the ratings dataframe has a user id associated with at least one movie, a rating and a timestamp showing when they reviewed it. We won't be needing the timestamp column, so let's drop it to save on memory."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ad0NqHaccFp6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "93d4973b-64cb-41cd-cf5d-1a496f13effe"
      },
      "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": 20,
      "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",
              "    </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": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0k5q29PcFp8",
        "colab_type": "text"
      },
      "source": [
        "<a id=\"ref3\"></a>\n",
        "# Content-Based recommendation system"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yJ-LaOUcFp9",
        "colab_type": "text"
      },
      "source": [
        "Now, let's take a look at how to implement __Content-Based__ or __Item-Item recommendation systems__. 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",
        "Notice: To add more movies, simply increase the amount of elements in the __userInput__. Feel free to add more in! Just be sure to write it in with capital letters and if a movie starts with a \"The\", like \"The Matrix\" then write it in like this: 'Matrix, The' ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMrh0vT3cFp-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "764efcaa-6bfb-4863-9013-2a9267c6ffc7"
      },
      "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",
        "            {'title':'Waiting to Exhale','rating':4.5},\n",
        "            {'title':'Avengers, The', 'rating':5.0}\n",
        "         ] \n",
        "inputMovies = pd.DataFrame(userInput)\n",
        "inputMovies"
      ],
      "execution_count": 36,
      "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>rating</th>\n",
              "      <th>title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Breakfast Club, The</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.5</td>\n",
              "      <td>Toy Story</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>Jumanji</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.5</td>\n",
              "      <td>Akira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.5</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.0</td>\n",
              "      <td>Avengers, The</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   rating                title\n",
              "0     5.0  Breakfast Club, The\n",
              "1     3.5            Toy Story\n",
              "2     2.0              Jumanji\n",
              "3     5.0         Pulp Fiction\n",
              "4     4.5                Akira\n",
              "5     4.5    Waiting to Exhale\n",
              "6     5.0        Avengers, The"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XILV74xRcFqA",
        "colab_type": "text"
      },
      "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",
        "We can achieve this by first filtering out the rows that contain the input movie's title and then merging this subset with the input dataframe. We also drop unnecessary columns for the input to save memory space."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "ibw9YxXAcFqB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "0d7cfb8f-f84d-4af1-d639-d8385a8e3225"
      },
      "source": [
        "#Filtering out the movies by title\n",
        "inputId = movies_df[movies_df['title'].isin(inputMovies['title'].tolist())]\n",
        "inputId"
      ],
      "execution_count": 37,
      "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>0</th>\n",
              "      <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>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>293</th>\n",
              "      <td>296</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>[Comedy, Crime, Drama, Thriller]</td>\n",
              "      <td>1994</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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2070</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure]</td>\n",
              "      <td>1998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17914</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       movieId  ...  year\n",
              "0            1  ...  1995\n",
              "1            2  ...  1995\n",
              "3            4  ...  1995\n",
              "293        296  ...  1994\n",
              "1246      1274  ...  1988\n",
              "1885      1968  ...  1985\n",
              "2070      2153  ...  1998\n",
              "17914    89745  ...  2012\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoP6DHWrxP60",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "0cbd6d23-cf98-4e2a-e237-17440dcab052"
      },
      "source": [
        "#Then merging it so we can get the movieId. It's implicitly merging it by title.\n",
        "inputMovies = pd.merge(inputId, inputMovies)\n",
        "inputMovies"
      ],
      "execution_count": 38,
      "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>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>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
              "      <td>1995</td>\n",
              "      <td>3.5</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>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>[Comedy, Drama, Romance]</td>\n",
              "      <td>1995</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>296</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>[Comedy, Crime, Drama, Thriller]</td>\n",
              "      <td>1994</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1274</td>\n",
              "      <td>Akira</td>\n",
              "      <td>[Action, Adventure, Animation, Sci-Fi]</td>\n",
              "      <td>1988</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1968</td>\n",
              "      <td>Breakfast Club, The</td>\n",
              "      <td>[Comedy, Drama]</td>\n",
              "      <td>1985</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure]</td>\n",
              "      <td>1998</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
              "      <td>2012</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movieId                title  ...  year rating\n",
              "0        1            Toy Story  ...  1995    3.5\n",
              "1        2              Jumanji  ...  1995    2.0\n",
              "2        4    Waiting to Exhale  ...  1995    4.5\n",
              "3      296         Pulp Fiction  ...  1994    5.0\n",
              "4     1274                Akira  ...  1988    4.5\n",
              "5     1968  Breakfast Club, The  ...  1985    5.0\n",
              "6     2153        Avengers, The  ...  1998    5.0\n",
              "7    89745        Avengers, The  ...  2012    5.0\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6aeWjVgxsx1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "59dc291a-c154-46ef-b6bf-3a68fbbf2105"
      },
      "source": [
        "#Dropping information we won't use from the input dataframe\n",
        "inputMovies = inputMovies.drop(['genres','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": 39,
      "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>4</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>296</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1274</td>\n",
              "      <td>Akira</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1968</td>\n",
              "      <td>Breakfast Club, The</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, 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        4    Waiting to Exhale     4.5\n",
              "3      296         Pulp Fiction     5.0\n",
              "4     1274                Akira     4.5\n",
              "5     1968  Breakfast Club, The     5.0\n",
              "6     2153        Avengers, The     5.0\n",
              "7    89745        Avengers, The     5.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_oMSsiFcFqD",
        "colab_type": "text"
      },
      "source": [
        "We're going to start by learning the input's preferences, so let's get the subset of movies that the input has watched from the Dataframe containing genres defined with binary values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fg9-UQYcFqE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "e38c6443-48e8-473c-ad3a-1658b6e7320e"
      },
      "source": [
        "#Filtering out the movies from the input\n",
        "userMovies = moviesWithGenres_df[moviesWithGenres_df['movieId'].isin(inputMovies['movieId'].tolist())]\n",
        "userMovies"
      ],
      "execution_count": 40,
      "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>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>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",
              "    <tr>\n",
              "      <th>2070</th>\n",
              "      <td>2153</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure]</td>\n",
              "      <td>1998</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17914</th>\n",
              "      <td>89745</td>\n",
              "      <td>Avengers, The</td>\n",
              "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
              "      <td>2012</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>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>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",
              "    </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",
              "3            4    Waiting to Exhale  ...       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",
              "2070      2153        Avengers, The  ...       0.0                0.0\n",
              "17914    89745        Avengers, The  ...       0.0                0.0\n",
              "\n",
              "[8 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQh5EKec21wM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6dc948a5-3c81-4e9e-d5f4-59c3912ed13e"
      },
      "source": [
        "userMovies.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AUSOTsWqcFqH",
        "colab_type": "text"
      },
      "source": [
        "We'll only need the actual genre table, so let's clean this up a bit by resetting the index and dropping the movieId, title, genres and year columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1MtLpQfhcFqI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "7ce6fe99-24c9-470e-87ae-cb645a55950c"
      },
      "source": [
        "#Resetting the index to avoid future issues\n",
        "userMovies = userMovies.reset_index(drop=True)\n",
        "userMovies.head()"
      ],
      "execution_count": 42,
      "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",
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              "      <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",
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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>2</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",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</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>4</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",
              "  </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        4  Waiting to Exhale  ...       0.0                0.0\n",
              "3      296       Pulp Fiction  ...       0.0                0.0\n",
              "4     1274              Akira  ...       0.0                0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqN0qaqR3JtX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "00e4d4d9-8cb5-4452-da58-e845d71ce24e"
      },
      "source": [
        "#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 = userMovies.drop(['movieId','title','genres','year'],1)\n",
        "userGenreTable"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
              "      <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>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>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>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>3</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>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>4</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>5</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",
              "    <tr>\n",
              "      <th>6</th>\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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\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>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>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",
              "    </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        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "4        1.0        1.0       0.0  ...      0.0        0.0                 0.0\n",
              "5        0.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "6        1.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "7        1.0        0.0       0.0  ...      0.0        0.0                 0.0\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdWCOIeT6Zj7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8396d4f0-5d4f-4bbd-f8e4-043a7383ea59"
      },
      "source": [
        "userGenreTable.shape"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxgh9tihcFqL",
        "colab_type": "text"
      },
      "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": {
        "id": "hqevx3ShcFqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "e3463a2d-504e-491f-9071-28a37e2d9c9c"
      },
      "source": [
        "inputMovies['rating']"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    3.5\n",
              "1    2.0\n",
              "2    4.5\n",
              "3    5.0\n",
              "4    4.5\n",
              "5    5.0\n",
              "6    5.0\n",
              "7    5.0\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAAgbW0ocFqQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "036013af-0681-4d8b-ce96-9074a4e287e7"
      },
      "source": [
        "#Dot produt to get weights\n",
        "userProfile = userGenreTable.transpose().dot(inputMovies['rating'])\n",
        "#The user profile\n",
        "userProfile"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adventure             20.0\n",
              "Animation              8.0\n",
              "Children               5.5\n",
              "Comedy                18.0\n",
              "Fantasy                5.5\n",
              "Romance                4.5\n",
              "Drama                 14.5\n",
              "Action                14.5\n",
              "Crime                  5.0\n",
              "Thriller               5.0\n",
              "Horror                 0.0\n",
              "Mystery                0.0\n",
              "Sci-Fi                 9.5\n",
              "IMAX                   5.0\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": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miwAT8xe6TRq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "86d4f871-4025-4860-c87e-f6f80835ff24"
      },
      "source": [
        "userProfile.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USaw4jSlcFqW",
        "colab_type": "text"
      },
      "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": "4M34ruWXcFqX",
        "colab_type": "text"
      },
      "source": [
        "Let's start by extracting the genre table from the original dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4HZQ344cFqY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "f6629b48-b0b0-4f6a-8628-4fef4cc7117c"
      },
      "source": [
        "#Now let's get the genres of every movie in our original dataframe\n",
        "genreTable = moviesWithGenres_df.set_index(moviesWithGenres_df['movieId'])\n",
        "genreTable.head()"
      ],
      "execution_count": 48,
      "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",
              "    <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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</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>2</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>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Comedy, 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>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>4</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>5</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",
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            ],
            "text/plain": [
              "         movieId                        title  ... Film-Noir (no genres listed)\n",
              "movieId                                        ...                             \n",
              "1              1                    Toy Story  ...       0.0                0.0\n",
              "2              2                      Jumanji  ...       0.0                0.0\n",
              "3              3             Grumpier Old Men  ...       0.0                0.0\n",
              "4              4            Waiting to Exhale  ...       0.0                0.0\n",
              "5              5  Father of the Bride Part II  ...       0.0                0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDCVD_dj63yO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "a666a756-9d07-4d5b-881c-21c9ec89c61e"
      },
      "source": [
        "#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": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adventure</th>\n",
              "      <th>Animation</th>\n",
              "      <th>Children</th>\n",
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              "      <th>(no genres listed)</th>\n",
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            "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": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F_pvwWVcFqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22feaf89-9f1b-4d18-973a-6b418cbd241e"
      },
      "source": [
        "genreTable.shape"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34208, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eGk8ROEcFqc",
        "colab_type": "text"
      },
      "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": {
        "id": "ADf-Vo-CcFqc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "522d4294-c7b6-49d4-b9c7-c128304ce704"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "1    0.495652\n",
              "2    0.269565\n",
              "3    0.195652\n",
              "4    0.321739\n",
              "5    0.156522\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCk9FUOV_cIp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e5a60624-1dd3-425a-a5f5-c5f5e5578e1c"
      },
      "source": [
        "recommendationTable_df.shape"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34208,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3WYl29ycFqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "df6e49e1-732b-4e00-e5fc-dfe54118ff7a"
      },
      "source": [
        "#Sort our recommendations in descending order\n",
        "recommendationTable_df = recommendationTable_df.sort_values(ascending=False)\n",
        "#Just a peek at the values\n",
        "recommendationTable_df.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieId\n",
              "27344     0.739130\n",
              "85261     0.700000\n",
              "5018      0.673913\n",
              "117646    0.673913\n",
              "122787    0.669565\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8aRaP78cFqh",
        "colab_type": "text"
      },
      "source": [
        "Now here's the recommendation table!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "v2YAmAG_cFqi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "outputId": "52cbb32d-4410-4d2b-89dd-9c1938848008"
      },
      "source": [
        "#The final recommendation table\n",
        "movies_df.loc[movies_df['movieId'].isin(recommendationTable_df.head(20).keys())]  # Adding the .keys() displays all the values not just the headers."
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
              "      <th>4861</th>\n",
              "      <td>4956</td>\n",
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              "      <th>11806</th>\n",
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              "      <th>13109</th>\n",
              "      <td>62956</td>\n",
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              "      <td>2008</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",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>14397</th>\n",
              "      <td>71999</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "    <tr>\n",
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              "      <th>16884</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>122787</td>\n",
              "      <td>The 39 Steps</td>\n",
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            ],
            "text/plain": [
              "       movieId  ...  year\n",
              "542        546  ...  1993\n",
              "4861      4956  ...  1980\n",
              "4923      5018  ...  1991\n",
              "6793      6902  ...  2002\n",
              "8605     26093  ...  1962\n",
              "9296     27344  ...  1999\n",
              "10575    40339  ...  2005\n",
              "11785    52287  ...  2007\n",
              "11806    52462  ...  2007\n",
              "12021    54278  ...  2007\n",
              "13109    62956  ...  2008\n",
              "13250    64645  ...  1968\n",
              "14397    71999  ...  1924\n",
              "16055    81132  ...  2010\n",
              "16504    83266  ...  2000\n",
              "16884    85261  ...  2011\n",
              "22145   106240  ...  2013\n",
              "25218   117646  ...  2000\n",
              "26442   122787  ...  1959\n",
              "33509   148775  ...  2009\n",
              "\n",
              "[20 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPGKtptgcFqk",
        "colab_type": "text"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r81EhLYMcFqk",
        "colab_type": "text"
      },
      "source": [
        "<h2>Want to learn more?</h2>\n",
        "\n",
        "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
        "\n",
        "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
        "\n",
        "<h3>Thanks for completing this lesson!</h3>\n",
        "\n",
        "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
        "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
        "\n",
        "<hr>\n",
        "\n",
        "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p> "
      ]
    }
  ]
}