movie_recommender.ipynb

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        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ToM4tto/MovieCompanion/blob/main/movie_recommender.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CSD3185 Group 26 Machine Learning Project**: \n",
        "## MovieCompanion - A Movie Recommendation System"
      ],
      "metadata": {
        "id": "Amk3ch0fSJqT"
      },
      "id": "Amk3ch0fSJqT"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mounting Google Drive"
      ],
      "metadata": {
        "id": "mqtG76JCSj89"
      },
      "id": "mqtG76JCSj89"
    },
    {
      "cell_type": "code",
      "source": [
        "#Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "#This will prompt for authorisation\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7G37Tl6DXs25",
        "outputId": "1e5f22fc-89cb-45f3-cdca-1c2e50897d77"
      },
      "id": "7G37Tl6DXs25",
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Data\n",
        "**The Movies Dataset** - Metadata on over 45,000 movies. 26 million ratings from over 270,000 users: https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset\n",
        "\n",
        "The following files from the dataset were used:\n",
        "- credits.csv\n",
        "- keywords.csv\n",
        "- links_small.csv\n",
        "- movies_metadata.csv\n",
        "- ratings_small.csv\n",
        "\n",
        "Make sure you have the datasets in a folder \"data\" within your Google Drive when running this project\n",
        "\n"
      ],
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        "id": "jsUoWJeIksD7"
      },
      "id": "jsUoWJeIksD7"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from ast import literal_eval \n",
        "\n",
        "df1 = pd.read_csv(\"/content/drive/MyDrive/data/movies_metadata.csv\", low_memory=False)\n",
        "df2 = pd.read_csv(\"/content/drive/MyDrive/data/credits.csv\")\n",
        "df3 = pd.read_csv(\"/content/drive/MyDrive/data/keywords.csv\")\n",
        "links_small = pd.read_csv(\"/content/drive/MyDrive/data/links_small.csv\")\n",
        "\n",
        "def convert_int(x):\n",
        "    try:\n",
        "        return int(x)\n",
        "    except:\n",
        "        return np.nan\n",
        "\n",
        "##### CLEAN DATA #####\n",
        "#df1['genres'] = df1['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n",
        "\n",
        "# Apply the convert_int function to the 'id' column in df1\n",
        "df1['id'] = df1['id'].apply(convert_int) #some id cannot be converted to int, data elements should be removed\n",
        "\n",
        "# Drop rows with NaN values in the 'id' column\n",
        "df1.dropna(subset=['id'], inplace=True)\n",
        "\n",
        "# Convert all ids to int\n",
        "df1['id'] = df1['id'].astype('int')\n",
        "df2['id'] = df2['id'].astype('int')\n",
        "df3['id'] = df3['id'].astype('int')\n",
        "links_small = links_small[links_small['tmdbId'].notnull()]['tmdbId'].astype('int')\n",
        "\n",
        "# Merge the two DataFrames on the 'id' column\n",
        "df1 = df1.merge(df2,on='id')\n",
        "df1 = df1.merge(df3,on='id')\n",
        "\n",
        "df1 = df1[df1['id'].isin(links_small)] # REDUCE 45K DATASET TO 9K (not enough resources in free Colab environment)\n",
        "df1 = df1.reset_index()\n",
        "#Peek our data\n",
        "df1.head(3)"
      ],
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      "outputs": [
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              "   index  adult                              belongs_to_collection    budget  \\\n",
              "0      0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
              "1      1  False                                                NaN  65000000   \n",
              "2      2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
              "\n",
              "                                              genres  \\\n",
              "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
              "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
              "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
              "\n",
              "                               homepage     id    imdb_id original_language  \\\n",
              "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
              "1                                   NaN   8844  tt0113497                en   \n",
              "2                                   NaN  15602  tt0113228                en   \n",
              "\n",
              "     original_title  ...                                   spoken_languages  \\\n",
              "0         Toy Story  ...           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
              "1           Jumanji  ...  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
              "2  Grumpier Old Men  ...           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
              "\n",
              "     status                                            tagline  \\\n",
              "0  Released                                                NaN   \n",
              "1  Released          Roll the dice and unleash the excitement!   \n",
              "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
              "\n",
              "              title  video vote_average  vote_count  \\\n",
              "0         Toy Story  False          7.7      5415.0   \n",
              "1           Jumanji  False          6.9      2413.0   \n",
              "2  Grumpier Old Men  False          6.5        92.0   \n",
              "\n",
              "                                                cast  \\\n",
              "0  [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
              "1  [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
              "2  [{'cast_id': 2, 'character': 'Max Goldman', 'c...   \n",
              "\n",
              "                                                crew  \\\n",
              "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...   \n",
              "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   \n",
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              "\n",
              "                                            keywords  \n",
              "0  [{'id': 931, 'name': 'jealousy'}, {'id': 4290,...  \n",
              "1  [{'id': 10090, 'name': 'board game'}, {'id': 1...  \n",
              "2  [{'id': 1495, 'name': 'fishing'}, {'id': 12392...  \n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualising Data (optional)"
      ],
      "metadata": {
        "id": "iPrX1L6DriHD"
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      "id": "iPrX1L6DriHD"
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#enable graph render inline on Jupyter notebooks\n",
        "%matplotlib inline \n",
        "\n",
        "#Make smaller dataframe with lesser cols\n",
        "ratings = df1[[\"title\", \"vote_average\", \"vote_count\"]]\n",
        "\n",
        "# Sort the DataFrame by highest vote_score given the highest vote_count\n",
        "ratings = ratings.sort_values(['vote_count', 'vote_average'], ascending=[False, False])\n",
        "\n",
        "#Peek at our data\n",
        "ratings.head(5)"
      ],
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            "text/plain": [
              "                title  vote_average  vote_count\n",
              "7648        Inception           8.1     14075.0\n",
              "6981  The Dark Knight           8.3     12269.0\n",
              "7488           Avatar           7.2     12114.0\n",
              "7969     The Avengers           7.4     12000.0\n",
              "8871         Deadpool           7.4     11444.0"
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f4902ac-949b-4930-b791-1ee16f7641a1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1f4902ac-949b-4930-b791-1ee16f7641a1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Print Distribution of vote_count\n",
        "print(\"Distribution of Vote_Count (frequency vs vote_count)\")\n",
        "ratings['vote_count'].hist(bins=80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "dv0T3gcf0bEs",
        "outputId": "14be0368-23e5-4d59-a349-a97470fb5d5d"
      },
      "id": "dv0T3gcf0bEs",
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of Vote_Count (frequency vs vote_count)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Print Distribution of Average Votes\n",
        "print(\"Distribution of Average Vote (frequency vs vote_average)\")\n",
        "ratings['vote_average'].hist(bins=80)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "d61xtu1Iy7bz",
        "outputId": "e809f20e-1474-4451-9663-9934db4ebf36"
      },
      "id": "d61xtu1Iy7bz",
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of Average Vote (frequency vs vote_average)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Algorithm 1:** Content-based Filtering"
      ],
      "metadata": {
        "id": "EakjzKzR2zgm"
      },
      "id": "EakjzKzR2zgm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will be computing scores for all movies based on different features (Overview, Credits, Genres and Keywords) in order to make good recommendations.\n",
        "\n",
        "We need to compute the **Term Frequency-Inverse Document Frequency (TF-IDF) vectors**  for each plot overview. The TF-IDF score for a given word in a given document is high if that word appears frequently in that document but infrequently in the other documents. Conversely, the score is low if the word appears frequently in many documents in the corpus. By calculating the TF-IDF score for each word in each document, we can identify the words that are most strongly associated with each document, which can be useful for tasks such as document classification or information retrieval.\n",
        "\n",
        "We will obtain a **TF-IDF matrix**  where each column represents a word in the combined set of movie overviews and each row represents a movie. Each element would represent the TFIDF score of the word in col for the movie in row. This allows us to filter significant words and therefore their significance to the various movies, helping us in our recommendation."
      ],
      "metadata": {
        "id": "jcaRtENy31xE"
      },
      "id": "jcaRtENy31xE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Getting plot overview**"
      ],
      "metadata": {
        "id": "I3rD2ivNYxUy"
      },
      "id": "I3rD2ivNYxUy"
    },
    {
      "cell_type": "code",
      "source": [
        "#Compute Term Frequency-Inverse Document Frequency (TF-IDF) vectors for each plot overview\n",
        "#Import TfIdfVectorizer from scikit-learn\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#Define a TF-IDF Vectorizer Object. Remove common english stop words e.g: \"the\", \"a\"\n",
        "tfidf = TfidfVectorizer(stop_words = \"english\")\n",
        "\n",
        "#Clean Data by removing NaN empty string\n",
        "df1[\"overview\"] = df1[\"overview\"].fillna(\"\")\n",
        "\n",
        "#Construct TF-IDF matrix by fitting and transforming the data (text) into a TF-IDF matrix representation\n",
        "tfidf_matrix = tfidf.fit_transform(df1[\"overview\"])\n",
        "\n",
        "#Output the shape of tfidf_matrix\n",
        "# 20978 different words describe 4803 movies in our data\n",
        "tfidf_matrix.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qtQZytn3s3M",
        "outputId": "ab4ee7fc-c87a-4f8f-a87d-379e2b7724c6"
      },
      "id": "_qtQZytn3s3M",
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9219, 29727)"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculate the Cosine Similarity: "
      ],
      "metadata": {
        "id": "-lt93aZyE1tk"
      },
      "id": "-lt93aZyE1tk"
    },
    {
      "cell_type": "code",
      "source": [
        "#Import linear_kernel (dot product)\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "#Cosine similarity is the dot product between two normalized vectors, the similarity between all pairs of movies in the dataset\n",
        "#each element (i,j) in the cosine_sim matrix represents the dot product of the ith row of tfidf with the jth row of tfidf,\n",
        "#this calculates similarity with every other movie (row)\n",
        "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
        "\n",
        "#Create a mapping between movie titles and their indices in the dataset, dropping duplicate movie titles\n",
        "title_to_indices = pd.Series(df1.index, df1['title']).drop_duplicates()\n",
        "title_to_indices.shape"
      ],
      "metadata": {
        "id": "eX_uARl78dRB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5200763d-cfd6-4c71-ab48-0a77f74c8776"
      },
      "id": "eX_uARl78dRB",
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9219,)"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Simple search engine**\n",
        "- It just doesn't account for upper/lower case and stopwords."
      ],
      "metadata": {
        "id": "TNgbOk8pq0Ex"
      },
      "id": "TNgbOk8pq0Ex"
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "#from nltk.stem import SnowballStemmer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "searchDF = df1.copy()\n",
        "\n",
        "# Convert all text to lowercase\n",
        "searchDF['title'] = searchDF['title'].str.lower()\n",
        "\n",
        "# Remove stop words (common words such as \"the\", \"and\", \"a\")\n",
        "nltk.download('stopwords')\n",
        "stop_words = stopwords.words('english')\n",
        "searchDF['title'] = searchDF['title'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words]))\n",
        "\n",
        "# Stem the words (reduce each word to its root form)\n",
        "# stemmer = SnowballStemmer('english')\n",
        "# searchDF['title'] = searchDF['title'].apply(lambda x: ' '.join([stemmer.stem(word) for word in x.split()])) \n",
        "\n",
        "tfidf = TfidfVectorizer(analyzer='word', ngram_range=(1, 2), min_df=0, stop_words='english')\n",
        "tfidf_matrix = tfidf.fit_transform(searchDF['title'])\n",
        "\n",
        "search_cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
        "\n",
        "#Create a mapping between movie titles and their indices in the dataset, dropping duplicate movie titles\n",
        "title_to_indices_search = pd.Series(searchDF.index, searchDF['title']).drop_duplicates()\n",
        "\n",
        "def get_similar_titles(title, cosine_similarities, df):\n",
        "    title = title.lower()\n",
        "    cleaned_title = ' '.join([word for word in title.split() if word not in stop_words])\n",
        "    if cleaned_title not in searchDF['title'].values:\n",
        "        print(\"No movie with similar title\")\n",
        "        return\n",
        "    # Get the index of the given title\n",
        "    idx = title_to_indices_search[cleaned_title]\n",
        "\n",
        "    # Get the cosine similarities of the given title with all other titles\n",
        "    sim_scores = list(enumerate(cosine_similarities[idx]))\n",
        "\n",
        "    # If any of the cosine similarities are arrays, take their average\n",
        "    sim_scores = [(i, s.mean() if isinstance(s, np.ndarray) else s) for i, s in sim_scores]\n",
        "\n",
        "    # Sort the similarities in descending order\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the top 10 most similar titles (excluding the given title)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "    title_indices = [i[0] for i in sim_scores]\n",
        "    return df1['title'].iloc[title_indices]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RypI5ITqylP",
        "outputId": "349ffc51-49a9-4ea5-e3e9-4be2c9bdcbf3"
      },
      "id": "3RypI5ITqylP",
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Content-based Recommender function**"
      ],
      "metadata": {
        "id": "SNa_YDItq8Yq"
      },
      "id": "SNa_YDItq8Yq"
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that takes in movie title as input and outputs most similar movies\n",
        "def getRecommendations(title, cosine_sim, top=10):\n",
        "    # Error checking, invalid movie title\n",
        "    if (title not in title_to_indices.index):\n",
        "      print(\"Exact movie not found, searching for similar titles:\")\n",
        "      print(get_similar_titles(title, search_cosine_sim, df1))\n",
        "      return\n",
        "    # Index of the movie that has the title\n",
        "    index = title_to_indices[title]\n",
        "\n",
        "    # Get the pairwsie similarity scores of all movies with that movie\n",
        "    sim_scores = cosine_sim[index]\n",
        "\n",
        "    # Sort movies based on the similarity scores in desc order and return top 10 most similar\n",
        "    # Gets the array of indices if it were to be sorted\n",
        "    movie_indices = np.argsort(-sim_scores)[1:top+1]\n",
        "\n",
        "    # Return the top 10 most similar movies, gets location from df1\n",
        "    return pd.DataFrame({'movieId': movie_indices, 'title': df1.loc[movie_indices, 'title'].tolist()})\n"
      ],
      "metadata": {
        "id": "Cn_pPuQF-JiC"
      },
      "id": "Cn_pPuQF-JiC",
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "getRecommendations('Iron Man', cosine_sim, 10) #using just plot overview (we're not done yet, see below)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "h7vI5kGE-pkm",
        "outputId": "d8d8d348-0b74-4227-c710-108a6fa0dc22"
      },
      "id": "h7vI5kGE-pkm",
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                    title\n",
              "0     7600               Iron Man 2\n",
              "1     8392               Iron Man 3\n",
              "2     5730                 Scarface\n",
              "3     8868  Avengers: Age of Ultron\n",
              "4     8190                    Brake\n",
              "5     6129                  Hostage\n",
              "6     2340            The Dark Half\n",
              "7     4316     Saturday Night Fever\n",
              "8     6218            Batman Begins\n",
              "9     1019            Touch of Evil"
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              "      <td>8190</td>\n",
              "      <td>Brake</td>\n",
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              "      <td>6218</td>\n",
              "      <td>Batman Begins</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1019</td>\n",
              "      <td>Touch of Evil</td>\n",
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              "          const element = document.querySelector('#df-854745c6-d368-49bb-a1ef-39cdd565a9dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**This code changes dataframe, run this code once!**"
      ],
      "metadata": {
        "id": "T_TORXbBWpU_"
      },
      "id": "T_TORXbBWpU_"
    },
    {
      "cell_type": "code",
      "source": [
        "# Parse the stringified features into their corresponding python objects\n",
        "features = ['cast', 'crew', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "    df1[feature] = df1[feature].apply(literal_eval)\n",
        "\n",
        "# Get Director from \"crew\" feature lists\n",
        "def getDirector(crews):\n",
        "    for crew in crews:\n",
        "        #if isinstance(crew, dict):\n",
        "          if crew['job'] == 'Director':\n",
        "            return crew['name']\n",
        "    return np.nan\n",
        "\n",
        "# Returns Top 3 in \"cast\", \"keywords\", \"genres\" feature lists\n",
        "\n",
        "def getTop3(x):\n",
        "    if isinstance(x, list):\n",
        "        names = [i['name'] for i in x]\n",
        "        #Check if > 3 elements. If no, returns entire list. \n",
        "        if len(names) >= 3:\n",
        "            names = names[:3]\n",
        "        return names\n",
        "    #Return empty list in case of missing/malformed data\n",
        "    return []\n",
        "\n",
        "features = ['cast', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "    df1[feature] = df1[feature].apply(getTop3) # Apply function along an axis of the DataFrame, each object passed to function should be a Series list\n",
        "\n",
        "# Set director, cast, genres and keywords features in a suitable format.\n",
        "df1['director'] = df1['crew'].apply(getDirector)\n",
        "\n",
        "# Print the new features of the first 3 films\n",
        "df1.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "8NIXiQXrYigI",
        "outputId": "8b6543f6-6262-4000-d461-df91981d2931"
      },
      "id": "8NIXiQXrYigI",
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index  adult                              belongs_to_collection    budget  \\\n",
              "0      0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
              "1      1  False                                                NaN  65000000   \n",
              "2      2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
              "\n",
              "                         genres                              homepage     id  \\\n",
              "0   [Animation, Comedy, Family]  http://toystory.disney.com/toy-story    862   \n",
              "1  [Adventure, Fantasy, Family]                                   NaN   8844   \n",
              "2             [Romance, Comedy]                                   NaN  15602   \n",
              "\n",
              "     imdb_id original_language    original_title  ...    status  \\\n",
              "0  tt0114709                en         Toy Story  ...  Released   \n",
              "1  tt0113497                en           Jumanji  ...  Released   \n",
              "2  tt0113228                en  Grumpier Old Men  ...  Released   \n",
              "\n",
              "                                             tagline             title  video  \\\n",
              "0                                                NaN         Toy Story  False   \n",
              "1          Roll the dice and unleash the excitement!           Jumanji  False   \n",
              "2  Still Yelling. Still Fighting. Still Ready for...  Grumpier Old Men  False   \n",
              "\n",
              "  vote_average vote_count                                            cast  \\\n",
              "0          7.7     5415.0             [Tom Hanks, Tim Allen, Don Rickles]   \n",
              "1          6.9     2413.0  [Robin Williams, Jonathan Hyde, Kirsten Dunst]   \n",
              "2          6.5       92.0      [Walter Matthau, Jack Lemmon, Ann-Margret]   \n",
              "\n",
              "                                                crew  \\\n",
              "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...   \n",
              "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   \n",
              "2  [{'credit_id': '52fe466a9251416c75077a89', 'de...   \n",
              "\n",
              "                                            keywords       director  \n",
              "0                               [jealousy, toy, boy]  John Lasseter  \n",
              "1  [board game, disappearance, based on children'...   Joe Johnston  \n",
              "2       [fishing, best friend, duringcreditsstinger]  Howard Deutch  \n",
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              "<p>3 rows × 29 columns</p>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91bd96bb-6469-4272-9202-5237b45f6218');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Clean Data by converting ALL strings to lowercase and remove spaces\n",
        "def clean_data(x):\n",
        "    if isinstance(x, list):\n",
        "      return [str.lower(i.replace(\" \", \"\")) for i in x]\n",
        "    else:\n",
        "      #Check if Director exists. If not, return empty string\n",
        "      if isinstance(x, str):\n",
        "        return str.lower(x.replace(\" \", \"\"))\n",
        "      else:\n",
        "        return \"\"\n",
        "\n",
        "features = ['cast', 'keywords', 'director', 'genres']\n",
        "# Apply clean_data function to your features\n",
        "for feature in features:\n",
        "  df1[feature] = df1[feature].apply(clean_data)\n",
        "\n",
        "def collectiveSoup(x):\n",
        "    return \" \".join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])\n",
        "df1['soup'] = df1.apply(collectiveSoup, axis=1) #combine the top 3 in 1 string in a new dataframe"
      ],
      "metadata": {
        "id": "ho_v8QNejcJ8"
      },
      "id": "ho_v8QNejcJ8",
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "CountVectorizer() instead of TF-IDF as we do not want to reduce the significance of an actor/director if they appeared in more movies (TF-IDF would reduce), we just want count of each word in `soup`.\n",
        "**Changing dataframe, run this code once!**"
      ],
      "metadata": {
        "id": "exGCyNsHkuwf"
      },
      "id": "exGCyNsHkuwf"
    },
    {
      "cell_type": "code",
      "source": [
        "# Import CountVectorizer and create the count matrix for each unique word in soup\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count = CountVectorizer(stop_words='english')\n",
        "count_matrix = count.fit_transform(df1['soup']) #matrix has 1 row per movie and 1 column per unique word in the soup, each element would be the frequency each unique word appeared in soup\n",
        "\n",
        "#Cosine similarity is the dot product between two normalized vectors (normalised automatically), the similarity between all pairs of movies in the dataset\n",
        "#each element (i,j) in the cosine_sim2 matrix represents the dot product of the ith row of count_matrix with the jth row of count_matrix,\n",
        "#this calculates similarity with every other movie (row)\n",
        "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)\n",
        "\n",
        "# Combined 'PLOT OVERVIEW' WITH 'cast', 'keywords', 'director', 'genres' similarities\n",
        "# Normalize the matrices\n",
        "normalized_cosine_sim1 = cosine_sim / cosine_sim.max()\n",
        "normalized_cosine_sim2 = cosine_sim2 / cosine_sim2.max()\n",
        "\n",
        "# Combine the similarity matrices using a weighted sum\n",
        "alpha = 0.5  # weight for tf-idf matrix\n",
        "beta = 0.5  # weight for count matrix\n",
        "combined_sim = alpha * normalized_cosine_sim1 + beta * normalized_cosine_sim2\n",
        "\n",
        "# Reset index and re-create mapping between movie titles and their indices in the dataset, dropping duplicates was already done\n",
        "df1 = df1.reset_index()\n",
        "indices = pd.Series(df1.index, index=df1['title'])"
      ],
      "metadata": {
        "id": "ZDsFC95Jkt8g"
      },
      "id": "ZDsFC95Jkt8g",
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(get_similar_titles(\"the iron man\", search_cosine_sim, df1)) #get similar movie titles\n",
        "getRecommendations(\"Iron Man\", combined_sim, 15) #get top 15 movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "2Ob6zy-zlLDn",
        "outputId": "a7218f47-45bf-4a33-8d04-358667c8342d"
      },
      "id": "2Ob6zy-zlLDn",
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7600                  Iron Man 2\n",
            "8392                  Iron Man 3\n",
            "3613        Tetsuo: The Iron Man\n",
            "4124                   Iron Will\n",
            "6045                      3-Iron\n",
            "781                 The Thin Man\n",
            "985                The Third Man\n",
            "3831    The Man Who Wasn't There\n",
            "5203                 Man on Fire\n",
            "5302            Another Thin Man\n",
            "Name: title, dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    movieId                       title\n",
              "0      7600                  Iron Man 2\n",
              "1      8392                  Iron Man 3\n",
              "2      8868     Avengers: Age of Ultron\n",
              "3      7764                TRON: Legacy\n",
              "4      8872  Captain America: Civil War\n",
              "5      1682                        Tron\n",
              "6      7969                The Avengers\n",
              "7      2594            Against All Odds\n",
              "8       232                   Star Wars\n",
              "9      8869                     Ant-Man\n",
              "10     1668  Return from Witch Mountain\n",
              "11     7208                   Replicant\n",
              "12     8414                 After Earth\n",
              "13     1816          Six-String Samurai\n",
              "14     1288                       Steel"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Algorithm 2:** Collaborative Filtering"
      ],
      "metadata": {
        "id": "DUboVh32ETMg"
      },
      "id": "DUboVh32ETMg"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Currently, our recommender is only able to recommend movies which are close to a certain movie. Different users keying in the same movie will get the exact same recommendations. Now, we want to make it more personal and account for the personal tastes and biases of a user.\n",
        "\n",
        "We will be using item-item (item-based) collaborative filtering instead of user-user (user-based) as items are simpler than users (more fundamental) and also users' preferences can change over time.\n",
        "\n",
        "Instead of measuring the similarity (Pearson Correlation or Cosine Similarity)between users, the item-item CF recommends items based on their similarity with other items that the target user rated (we will fill the users vs movies table vertically).\n",
        "\n",
        "Some issues with item-item method:\n",
        "1. Scalability - Computation grows with both the customer and the product. The worst case complexity is O(mn) with m users and n items.\n",
        "2. Sparsity - In extreme cases, we can have millions of users and the similarity between two fairly different movies could be very high simply because they have a similar rating for the only user who ranked them both.\n",
        "\n",
        "To minimise this we chose to turn it into an optimization problem. We can view it as how good we are in predicting the rating for items given a user. By using Root Mean Square Error (RMSE). The lower the RMSE, the better the performance.\n",
        "\n",
        "SVD decreases the dimension of a complex matrix by extracting its latent factors/features. Essentially, we map each user and each item into a latent space with dimension r. We will use Surprise library to implement the SVD."
      ],
      "metadata": {
        "id": "8Pxx8CYWEZ5s"
      },
      "id": "8Pxx8CYWEZ5s"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-surprise\n",
        "# !conda install -y -c conda-forge scikit-surprise # If you use conda on a non-Colab environment\n",
        "from surprise import Reader, Dataset, SVD\n",
        "from surprise.model_selection import cross_validate\n",
        "reader = Reader()\n",
        "ratings = pd.read_csv(\"/content/drive/MyDrive/data/ratings_small.csv\")\n",
        "ratings.head()\n",
        "\n",
        "data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
        "\n",
        "# Define the SVD algorithm\n",
        "svd = SVD()\n",
        "\n",
        "# Use cross_validate to evaluate the performance of the algorithm using 5-fold cross-validation\n",
        "results = cross_validate(svd, data, measures=['RMSE', 'MAE'], cv=5)\n",
        "\n",
        "# Print the results\n",
        "print(\"Evaluating RMSE, MAE of algorithm SVD.\\n\")\n",
        "for fold_idx, rmse, mae in zip(range(1, 6), results['test_rmse'], results['test_mae']):\n",
        "    print(f\"Fold {fold_idx}\")\n",
        "    print(f\"RMSE: {rmse:.4f}\")\n",
        "    print(f\"MAE:  {mae:.4f}\")\n",
        "    print(\"------------\")\n",
        "print(\"------------\")\n",
        "print(f\"Mean RMSE: {results['test_rmse'].mean():.4f}\") #Should be about 0.89 which is good enough for us\n",
        "print(f\"Mean MAE : {results['test_mae'].mean():.4f}\")\n",
        "print(\"------------\")\n",
        "\n",
        "trainset = data.build_full_trainset()\n",
        "svd.fit(trainset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toh2W_4wMVqm",
        "outputId": "c0237f23-501b-4620-c4fb-400229e66890"
      },
      "id": "toh2W_4wMVqm",
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-surprise in /usr/local/lib/python3.9/dist-packages (1.1.3)\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.9/dist-packages (from scikit-surprise) (1.10.1)\n",
            "Evaluating RMSE, MAE of algorithm SVD.\n",
            "\n",
            "Fold 1\n",
            "RMSE: 0.8982\n",
            "MAE:  0.6889\n",
            "------------\n",
            "Fold 2\n",
            "RMSE: 0.8971\n",
            "MAE:  0.6928\n",
            "------------\n",
            "Fold 3\n",
            "RMSE: 0.8971\n",
            "MAE:  0.6925\n",
            "------------\n",
            "Fold 4\n",
            "RMSE: 0.8909\n",
            "MAE:  0.6866\n",
            "------------\n",
            "Fold 5\n",
            "RMSE: 0.9012\n",
            "MAE:  0.6905\n",
            "------------\n",
            "------------\n",
            "Mean RMSE: 0.8969\n",
            "Mean MAE : 0.6902\n",
            "------------\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f1a50e16700>"
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          },
          "metadata": {},
          "execution_count": 170
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings[ratings['userId'] == 1] #Ratings userId 1 has given"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "PQB0zcxzT74a",
        "outputId": "6d382211-66ed-4b3c-abff-de3d694f358c"
      },
      "id": "PQB0zcxzT74a",
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    userId  movieId  rating   timestamp\n",
              "0        1       31     2.5  1260759144\n",
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              "15       1     2193     2.0  1260759198\n",
              "16       1     2294     2.0  1260759108\n",
              "17       1     2455     2.5  1260759113\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1129</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1172</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1263</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>1287</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1293</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>1339</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1260759125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>1343</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>1371</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1260759135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1260759203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>1953</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>2105</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>2150</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>2193</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>2294</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>2455</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1260759113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>2968</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1260759200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>3671</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "estimate = svd.predict(1, 302, 5).est #For user ID = 1, movie ID = 302, we get an estimated prediction of 2.68\n",
        "\n",
        "# Print the estimate and movie name\n",
        "print(f\"Estimated rating for Movie Id: 302: {estimate}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJfbFJ4wUGcC",
        "outputId": "4fde9ba3-104a-4b11-9b71-2752c2e41bbf"
      },
      "id": "aJfbFJ4wUGcC",
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Estimated rating for Movie Id: 302: 2.5878373935071224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id_map = pd.read_csv(\"/content/drive/MyDrive/data/links_small.csv\")[['movieId', 'tmdbId']]\n",
        "# Replace missing or infinite values with -1\n",
        "id_map['tmdbId'] = id_map['tmdbId'].fillna(-1)\n",
        "# convert tmdbId column to int\n",
        "id_map['tmdbId'] = id_map['tmdbId'].astype(int)\n",
        "\n",
        "# Merge the datasets based on tmdbId in id_map and id in id_map\n",
        "merged_dataset = pd.merge(id_map, df1, left_on='tmdbId', right_on='id')\n",
        "\n",
        "# Filter out rows with null values in the budget column\n",
        "filtered_df = merged_dataset[merged_dataset['title'].notnull()]\n",
        "#filtered_df.shape\n",
        "\n",
        "#estimate = svd.predict(1, 10, 3).est #For movie with ID 302, we get an estimated prediction of 2.618\n",
        "\n",
        "# Pick a user profile between 1 and 671, pick a movie to get predicted rating for\n",
        "def collaborative_predictor(title, userID = 1):\n",
        "  if (userID < 1 or userID > 671):\n",
        "    print(\"Please select a user group between 1 and 671\")\n",
        "    return\n",
        "  if title not in filtered_df[\"title\"].values:\n",
        "    print(\"Movie not even in dataset, cannot predict rating\")\n",
        "    return\n",
        "  movieId = filtered_df[filtered_df['title'] == title]['movieId'].values[0]\n",
        "  estimate = svd.predict(userID, movieId, 3).est\n",
        "  print(f\"User Group {userID}'s Estimated rating for {title} is : {estimate}\")\n",
        "\n",
        "# Pick a user profile between 1 and 671 and see the top rated movies rated by that user group\n",
        "def listTopMoviesRatedByUser(userID = 1, top = 5):\n",
        "    if (userID < 1 or userID > 671):\n",
        "      print(\"Please select a user group between 1 and 671\")\n",
        "      return\n",
        "    print(f\"Top movies previously rated by User Group {userID}: \")\n",
        "    user_ratings = ratings[ratings['userId'] == userID] #Ratings of movies in user groups\n",
        "    sorted_ratings = user_ratings.sort_values(by='rating', ascending=False)\n",
        "    top_ratings = sorted_ratings.head(top)\n",
        "     # Join with df1 to get the corresponding movie titles\n",
        "    top_movies = pd.merge(top_ratings, filtered_df[['title', 'movieId']], on='movieId', how='left')\n",
        "    # Print out only the rating and title\n",
        "    return top_movies[['title', 'rating']]\n",
        "\n",
        "# LETS PREDICT FOR USER 300\n",
        "userGroup = 300\n",
        "#Top movies rated by user profile:\n",
        "print(listTopMoviesRatedByUser(userGroup, 10))\n",
        "\n",
        "#Predict if userGroup will like Titanic or Aliens more\n",
        "collaborative_predictor(\"Titanic\", userGroup)\n",
        "collaborative_predictor(\"Iron Man\", userGroup)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "259Y-MttgN6s",
        "outputId": "1082122a-2f76-48dc-a903-086aac49fcd7"
      },
      "id": "259Y-MttgN6s",
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top movies previously rated by User Group 300: \n",
            "                                       title  rating\n",
            "0                                 Armageddon     5.0\n",
            "1                              Jurassic Park     5.0\n",
            "2  Star Wars: Episode I - The Phantom Menace     5.0\n",
            "3                                   The Rock     5.0\n",
            "4                         Planet of the Apes     5.0\n",
            "5                                 Braveheart     5.0\n",
            "6         Indiana Jones and the Last Crusade     4.5\n",
            "7                                  Cast Away     4.5\n",
            "8                                The Patriot     4.5\n",
            "9                     Mission: Impossible II     4.5\n",
            "User Group 300's Estimated rating for Titanic is : 3.950563976578467\n",
            "User Group 300's Estimated rating for Iron Man is : 4.5196137012633315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Algorithm 3:** Hybrid Algorithm"
      ],
      "metadata": {
        "id": "zlnHjlEvR70R"
      },
      "id": "zlnHjlEvR70R"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a mapping between movie titles and their corresponding movie IDs\n",
        "title_to_id = pd.Series(filtered_df['movieId'].values, index=filtered_df['title']).to_dict()\n",
        "\n",
        "# Define a function to obtain estimated ratings using the SVD model\n",
        "def get_estimated_rating(title, userID):\n",
        "    try:\n",
        "        movieId = title_to_id[title]\n",
        "        estimate = svd.predict(userID, movieId, 3).est\n",
        "        return estimate\n",
        "    except:\n",
        "        return None\n",
        "\n",
        "def hybrid(userId, title, top = 10):\n",
        "    if title not in filtered_df[\"title\"].values:\n",
        "      print(\"Movie not even in dataset, cannot predict rating\")\n",
        "      return\n",
        "\n",
        "    # 1. CONTENT-BASED FILTERING\n",
        "    # Index of the movie that has the title\n",
        "    # index = title_to_indices[title]\n",
        "    # # Get the pairwsie similarity scores of all movies with that movie\n",
        "    # sim_scores = list(enumerate(cosine_sim[index]))\n",
        "    # # Sort movies based on the similarity scores in desc order, skipping itself\n",
        "    # sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    # sim_scores = sim_scores[1:26]\n",
        "    # movie_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # movies = filtered_df.iloc[movie_indices][['title', 'id']] #content-based approach movies\n",
        "    # More movies to be selected in content-based step first to possibly include movies that did not make it in the top 10 content-based approach but align very closely to user group's tastes\n",
        "    movies = getRecommendations(title, combined_sim, top * 2)\n",
        "    \n",
        "    # 2. COLLABORATIVE FILTERING\n",
        "    # Apply the function to the 'title' column in the 'movies' dataframe\n",
        "    movies['Estimated Rating'] = movies['title'].apply(lambda x: get_estimated_rating(x, userId))\n",
        "\n",
        "    movies = movies.sort_values('Estimated Rating', ascending=False)\n",
        "    return print(movies.head(top))\n",
        "\n",
        "# Using it:\n",
        "user = 300\n",
        "# Step 1: Find movies that a user belong to user Group 300 may like:\n",
        "print(listTopMoviesRatedByUser(user))\n",
        "\n",
        "print(f\"\\nThese are the top 10 movies the user may like according to tastes of User Group {user}':\")\n",
        "# Step 2: Since user 300 rated Armageddon very highly, we pick movies similar to it but accounting for tastes of user group\n",
        "hybrid(user, 'Armageddon', 10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDju-iBbSKv8",
        "outputId": "d0d96f0c-8bbf-4f71-ebb3-9fb3f944ee6b"
      },
      "id": "aDju-iBbSKv8",
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top movies previously rated by User Group 300: \n",
            "                                       title  rating\n",
            "0                                 Armageddon     5.0\n",
            "1                              Jurassic Park     5.0\n",
            "2  Star Wars: Episode I - The Phantom Menace     5.0\n",
            "3                                   The Rock     5.0\n",
            "4                         Planet of the Apes     5.0\n",
            "\n",
            "These are the top 10 movies the user may like according to tastes of User Group 300':\n",
            "    movieId                 title  Estimated Rating\n",
            "5      1011        The Terminator          4.391232\n",
            "15     4349      The Professional          4.339073\n",
            "19     7691              The Town          4.222113\n",
            "4      6394          District B13          4.194526\n",
            "10     6716                  Next          4.147678\n",
            "1      7296  Terminator Salvation          4.134891\n",
            "11     6242            The Island          4.002384\n",
            "18     4150  The Sum of All Fears          3.898628\n",
            "9      8207                Looper          3.858136\n",
            "16      343               Timecop          3.838748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Conclusion:**\n",
        "\n",
        "Let's compare our algorithms:"
      ],
      "metadata": {
        "id": "rIAUDPaOa1ZO"
      },
      "id": "rIAUDPaOa1ZO"
    },
    {
      "cell_type": "code",
      "source": [
        "# User group\n",
        "user = 300\n",
        "# Step 1: Find movies that a user belong to user Group 300 may like:\n",
        "print(listTopMoviesRatedByUser(user, 10)) #can see that 'Armageddon' is the top rated movie preferred by this user group \n",
        "\n",
        "print(\"\\nThese are the top 10 movies similar to 'Armageddon' (Content-Based Approach):\\n\")\n",
        "print(getRecommendations('Armageddon', combined_sim, 10)) #get top 10 movies merely similar to Armageddon (content-based approach) without accounting for the user group's taste\n",
        "\n",
        "print(\"\\nThese are the top 10 movies similar to 'Armageddon' but sorted according to user group's taste (Hybrid Approach):\\n\")\n",
        "# Step 2: Since user 300 rated Armageddon very highly, let's pick movies similar to it\n",
        "hybrid(user, 'Armageddon', 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xz3YytoNbF-H",
        "outputId": "d5d527ce-e70f-4880-ebd1-f0dc2969f9c6"
      },
      "id": "Xz3YytoNbF-H",
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top movies previously rated by User Group 300: \n",
            "                                       title  rating\n",
            "0                                 Armageddon     5.0\n",
            "1                              Jurassic Park     5.0\n",
            "2  Star Wars: Episode I - The Phantom Menace     5.0\n",
            "3                                   The Rock     5.0\n",
            "4                         Planet of the Apes     5.0\n",
            "5                                 Braveheart     5.0\n",
            "6         Indiana Jones and the Last Crusade     4.5\n",
            "7                                  Cast Away     4.5\n",
            "8                                The Patriot     4.5\n",
            "9                     Mission: Impossible II     4.5\n",
            "\n",
            "These are the top 10 movies similar to 'Armageddon' (Content-Based Approach):\n",
            "\n",
            "   movieId                               title\n",
            "0     7907      Transformers: Dark of the Moon\n",
            "1     7296                Terminator Salvation\n",
            "2     4739  Terminator 3: Rise of the Machines\n",
            "3     7424                          Surrogates\n",
            "4     6394                        District B13\n",
            "5     1011                      The Terminator\n",
            "6     2040                              Meteor\n",
            "7     8854                  Terminator Genisys\n",
            "8     6738                            Sunshine\n",
            "9     8207                              Looper\n",
            "\n",
            "These are the top 10 movies similar to 'Armageddon' but sorted according to user group's taste (Hybrid Approach):\n",
            "\n",
            "    movieId                 title  Estimated Rating\n",
            "5      1011        The Terminator          4.391232\n",
            "15     4349      The Professional          4.339073\n",
            "19     7691              The Town          4.222113\n",
            "4      6394          District B13          4.194526\n",
            "10     6716                  Next          4.147678\n",
            "1      7296  Terminator Salvation          4.134891\n",
            "11     6242            The Island          4.002384\n",
            "18     4150  The Sum of All Fears          3.898628\n",
            "9      8207                Looper          3.858136\n",
            "16      343               Timecop          3.838748\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Purely using the **Content-based** approach will recommend the top 10 movies most similar to **\"Armageddon\"**. However, after accounting for user tastes (**Hybrid-approach**), we ended up with a different recommendation. We even got a different movie: **\"The Professional\"** that was not\n",
        " previously included in the Content-based approach. We predicted that the user would rate \"The Professional\" 4.4/5 if they have yet to rate it yet."
      ],
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