content_based_filtering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22a3270e-fee7-44e4-9362-c099f99ce8bb",
   "metadata": {},
   "source": [
    "# Content Based Filtering\n",
    "Author: Group 22"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca8c79c-f13f-4f39-8f0b-c9dbb66be021",
   "metadata": {},
   "source": [
    "## Warning: You sometimes need to upgrade the version of numpy if this code doesn't work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d1167eb-5858-4535-96a5-d011f376eaab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: numpy in /common/home/jss412/.local/lib/python3.9/site-packages (1.24.3)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    " pip install numpy --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288558d9-b768-47ce-beaa-684ef52fa287",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c18ba35-7ff3-4d6e-ac28-84ecdc604a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from itertools import combinations\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c733d618-c315-446d-86d9-078970046791",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings = \"ratings.dat\"\n",
    "users = \"users.dat\"\n",
    "movies = \"movies.dat\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9512f17-de15-4dc6-8edc-1b65b9529591",
   "metadata": {},
   "source": [
    "## Read Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5086f463-002d-405e-9542-501b44345e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_886012/4077109094.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_rating = pd.read_csv(\n"
     ]
    }
   ],
   "source": [
    "df_rating = pd.read_csv(\n",
    "    ratings,\n",
    "    sep=\"::\",\n",
    "    header=None, \n",
    "    names=['user_id', 'movie_id', 'rating', 'timestamp']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e6feee9-c760-4927-873c-22bbd8dfd8e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_886012/2253289803.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_user = pd.read_csv(\n"
     ]
    }
   ],
   "source": [
    "df_user = pd.read_csv(\n",
    "    users,\n",
    "    sep=\"::\",\n",
    "    header=None, \n",
    "    names=['user_id', 'gender', 'age', 'occupation', 'zipcode']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a3283317-8c77-41b1-8991-b6c5edabc31f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_886012/484467727.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  df_movie = pd.read_csv(\n"
     ]
    }
   ],
   "source": [
    "df_movie = pd.read_csv(\n",
    "    movies, \n",
    "    sep='::', \n",
    "    header=None, \n",
    "    names=['movie_id', 'title', 'genre'], \n",
    "    encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fa7a3f7c-9ae7-4892-aa5a-9f55fc666ff8",
   "metadata": {},
   "outputs": [
    {
     "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>user_id</th>\n",
       "      <th>movie_id</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>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1197</td>\n",
       "      <td>3</td>\n",
       "      <td>978302268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1287</td>\n",
       "      <td>5</td>\n",
       "      <td>978302039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>978300719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>978302268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>919</td>\n",
       "      <td>4</td>\n",
       "      <td>978301368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291\n",
       "5        1      1197       3  978302268\n",
       "6        1      1287       5  978302039\n",
       "7        1      2804       5  978300719\n",
       "8        1       594       4  978302268\n",
       "9        1       919       4  978301368"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d9571ba-a7f3-4fd8-a955-5ade419c011b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7002db6-1ffb-45f2-9ef1-cd22d38c4a33",
   "metadata": {},
   "outputs": [
    {
     "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</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>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|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</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                               title                         genre\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy\n",
       "5         6                         Heat (1995)         Action|Crime|Thriller\n",
       "6         7                      Sabrina (1995)                Comedy|Romance\n",
       "7         8                 Tom and Huck (1995)          Adventure|Children's\n",
       "8         9                 Sudden Death (1995)                        Action\n",
       "9        10                    GoldenEye (1995)     Action|Adventure|Thriller"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e87e3a-1e8f-4911-a5af-25fac0e44833",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b33f9b6-f64c-48f5-ae7e-7c9f245d0613",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_movie_ratings = df_rating.pivot(index='movie_id', columns='user_id', values='rating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd6d3d2d-3210-4687-8b20-2f14f954dce9",
   "metadata": {},
   "outputs": [
    {
     "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>6031</th>\n",
       "      <th>6032</th>\n",
       "      <th>6033</th>\n",
       "      <th>6034</th>\n",
       "      <th>6035</th>\n",
       "      <th>6036</th>\n",
       "      <th>6037</th>\n",
       "      <th>6038</th>\n",
       "      <th>6039</th>\n",
       "      <th>6040</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</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>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>5.0</td>\n",
       "      <td>...</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>3</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</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>8</th>\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>...</td>\n",
       "      <td>5.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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>...</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>10</th>\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>...</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",
       "<p>10 rows × 6040 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id   1     2     3     4     5     6     7     8     9     10    ...  \\\n",
       "movie_id                                                              ...   \n",
       "1          5.0   NaN   NaN   NaN   NaN   4.0   NaN   4.0   5.0   5.0  ...   \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0  ...   \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "6          NaN   NaN   NaN   NaN   2.0   NaN   4.0   NaN   NaN   NaN  ...   \n",
       "7          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   4.0  ...   \n",
       "8          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "9          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "10         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "user_id   6031  6032  6033  6034  6035  6036  6037  6038  6039  6040  \n",
       "movie_id                                                              \n",
       "1          NaN   4.0   NaN   NaN   4.0   NaN   NaN   NaN   NaN   3.0  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   2.0   2.0   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "6          NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN  \n",
       "7          NaN   NaN   NaN   NaN   3.0   NaN   NaN   NaN   NaN   NaN  \n",
       "8          5.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "9          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "10         NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[10 rows x 6040 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_movie_ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a568445-851e-4b2b-81ee-fe978282746c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3706, 6040)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_movie_ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30788758-1cd5-4a27-b00b-330a9cd9db40",
   "metadata": {},
   "source": [
    "## Creating a tdidf matrix based on movie genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab412e95-4d7e-4d12-9ea3-9337145b9880",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer=lambda s: (c for i in range(1,4)\n",
    "                                             for c in combinations(s.split('|'), r=i)))\n",
    "tfidf_matrix = tf.fit_transform(df_movie['genre'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6c134d46-0f4d-43f1-b5aa-f6727ad55c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3883x353 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 10729 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a760238-c1f9-477d-b5e6-871213d342e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3883, 353)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50fe8353-5ac0-45c0-a011-c06e2c01cf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim = cosine_similarity(tfidf_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "497ddb20-4f89-4e3d-8dfd-11daabc5057a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim_df = pd.DataFrame(cosine_sim, index=df_movie['title'], columns=df_movie['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "501692e1-30f3-4f41-b201-6986a0fac1ee",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "      <th>Toy Story (1995)</th>\n",
       "      <th>Jumanji (1995)</th>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <th>Heat (1995)</th>\n",
       "      <th>Sabrina (1995)</th>\n",
       "      <th>Tom and Huck (1995)</th>\n",
       "      <th>Sudden Death (1995)</th>\n",
       "      <th>GoldenEye (1995)</th>\n",
       "      <th>...</th>\n",
       "      <th>Bamboozled (2000)</th>\n",
       "      <th>Bootmen (2000)</th>\n",
       "      <th>Digimon: The Movie (2000)</th>\n",
       "      <th>Get Carter (2000)</th>\n",
       "      <th>Get Carter (1971)</th>\n",
       "      <th>Meet the Parents (2000)</th>\n",
       "      <th>Requiem for a Dream (2000)</th>\n",
       "      <th>Tigerland (2000)</th>\n",
       "      <th>Two Family House (2000)</th>\n",
       "      <th>Contender, The (2000)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</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>Toy Story (1995)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082508</td>\n",
       "      <td>0.067993</td>\n",
       "      <td>0.077516</td>\n",
       "      <td>0.171093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.067993</td>\n",
       "      <td>0.154340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171093</td>\n",
       "      <td>0.077516</td>\n",
       "      <td>0.336438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumanji (1995)</th>\n",
       "      <td>0.082508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.534583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.082905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.278515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grumpier Old Men (1995)</th>\n",
       "      <td>0.067993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180048</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.180048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Waiting to Exhale (1995)</th>\n",
       "      <td>0.077516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.180048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066577</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.127511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Father of the Bride Part II (1995)</th>\n",
       "      <td>0.171093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meet the Parents (2000)</th>\n",
       "      <td>0.171093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Requiem for a Dream (2000)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.169513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tigerland (2000)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.169513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two Family House (2000)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.392756</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.169513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contender, The (2000)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130644</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.522128</td>\n",
       "      <td>0.527915</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.324657</td>\n",
       "      <td>0.324657</td>\n",
       "      <td>0.324657</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 3883 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title                               Toy Story (1995)  Jumanji (1995)  \\\n",
       "title                                                                  \n",
       "Toy Story (1995)                            1.000000        0.082508   \n",
       "Jumanji (1995)                              0.082508        1.000000   \n",
       "Grumpier Old Men (1995)                     0.067993        0.000000   \n",
       "Waiting to Exhale (1995)                    0.077516        0.000000   \n",
       "Father of the Bride Part II (1995)          0.171093        0.000000   \n",
       "...                                              ...             ...   \n",
       "Meet the Parents (2000)                     0.171093        0.000000   \n",
       "Requiem for a Dream (2000)                  0.000000        0.000000   \n",
       "Tigerland (2000)                            0.000000        0.000000   \n",
       "Two Family House (2000)                     0.000000        0.000000   \n",
       "Contender, The (2000)                       0.000000        0.000000   \n",
       "\n",
       "title                               Grumpier Old Men (1995)  \\\n",
       "title                                                         \n",
       "Toy Story (1995)                                   0.067993   \n",
       "Jumanji (1995)                                     0.000000   \n",
       "Grumpier Old Men (1995)                            1.000000   \n",
       "Waiting to Exhale (1995)                           0.180048   \n",
       "Father of the Bride Part II (1995)                 0.397402   \n",
       "...                                                     ...   \n",
       "Meet the Parents (2000)                            0.397402   \n",
       "Requiem for a Dream (2000)                         0.000000   \n",
       "Tigerland (2000)                                   0.000000   \n",
       "Two Family House (2000)                            0.000000   \n",
       "Contender, The (2000)                              0.000000   \n",
       "\n",
       "title                               Waiting to Exhale (1995)  \\\n",
       "title                                                          \n",
       "Toy Story (1995)                                    0.077516   \n",
       "Jumanji (1995)                                      0.000000   \n",
       "Grumpier Old Men (1995)                             0.180048   \n",
       "Waiting to Exhale (1995)                            1.000000   \n",
       "Father of the Bride Part II (1995)                  0.453064   \n",
       "...                                                      ...   \n",
       "Meet the Parents (2000)                             0.453064   \n",
       "Requiem for a Dream (2000)                          0.392756   \n",
       "Tigerland (2000)                                    0.392756   \n",
       "Two Family House (2000)                             0.392756   \n",
       "Contender, The (2000)                               0.127511   \n",
       "\n",
       "title                               Father of the Bride Part II (1995)  \\\n",
       "title                                                                    \n",
       "Toy Story (1995)                                              0.171093   \n",
       "Jumanji (1995)                                                0.000000   \n",
       "Grumpier Old Men (1995)                                       0.397402   \n",
       "Waiting to Exhale (1995)                                      0.453064   \n",
       "Father of the Bride Part II (1995)                            1.000000   \n",
       "...                                                                ...   \n",
       "Meet the Parents (2000)                                       1.000000   \n",
       "Requiem for a Dream (2000)                                    0.000000   \n",
       "Tigerland (2000)                                              0.000000   \n",
       "Two Family House (2000)                                       0.000000   \n",
       "Contender, The (2000)                                         0.000000   \n",
       "\n",
       "title                               Heat (1995)  Sabrina (1995)  \\\n",
       "title                                                             \n",
       "Toy Story (1995)                       0.000000        0.067993   \n",
       "Jumanji (1995)                         0.000000        0.000000   \n",
       "Grumpier Old Men (1995)                0.000000        1.000000   \n",
       "Waiting to Exhale (1995)               0.000000        0.180048   \n",
       "Father of the Bride Part II (1995)     0.000000        0.397402   \n",
       "...                                         ...             ...   \n",
       "Meet the Parents (2000)                0.000000        0.397402   \n",
       "Requiem for a Dream (2000)             0.000000        0.000000   \n",
       "Tigerland (2000)                       0.000000        0.000000   \n",
       "Two Family House (2000)                0.000000        0.000000   \n",
       "Contender, The (2000)                  0.130644        0.000000   \n",
       "\n",
       "title                               Tom and Huck (1995)  Sudden Death (1995)  \\\n",
       "title                                                                          \n",
       "Toy Story (1995)                               0.154340                  0.0   \n",
       "Jumanji (1995)                                 0.534583                  0.0   \n",
       "Grumpier Old Men (1995)                        0.000000                  0.0   \n",
       "Waiting to Exhale (1995)                       0.000000                  0.0   \n",
       "Father of the Bride Part II (1995)             0.000000                  0.0   \n",
       "...                                                 ...                  ...   \n",
       "Meet the Parents (2000)                        0.000000                  0.0   \n",
       "Requiem for a Dream (2000)                     0.000000                  0.0   \n",
       "Tigerland (2000)                               0.000000                  0.0   \n",
       "Two Family House (2000)                        0.000000                  0.0   \n",
       "Contender, The (2000)                          0.000000                  0.0   \n",
       "\n",
       "title                               GoldenEye (1995)  ...  Bamboozled (2000)  \\\n",
       "title                                                 ...                      \n",
       "Toy Story (1995)                            0.000000  ...           0.171093   \n",
       "Jumanji (1995)                              0.082905  ...           0.000000   \n",
       "Grumpier Old Men (1995)                     0.000000  ...           0.397402   \n",
       "Waiting to Exhale (1995)                    0.000000  ...           0.453064   \n",
       "Father of the Bride Part II (1995)          0.000000  ...           1.000000   \n",
       "...                                              ...  ...                ...   \n",
       "Meet the Parents (2000)                     0.000000  ...           1.000000   \n",
       "Requiem for a Dream (2000)                  0.000000  ...           0.000000   \n",
       "Tigerland (2000)                            0.000000  ...           0.000000   \n",
       "Two Family House (2000)                     0.000000  ...           0.000000   \n",
       "Contender, The (2000)                       0.136534  ...           0.000000   \n",
       "\n",
       "title                               Bootmen (2000)  Digimon: The Movie (2000)  \\\n",
       "title                                                                           \n",
       "Toy Story (1995)                          0.077516                   0.336438   \n",
       "Jumanji (1995)                            0.000000                   0.278515   \n",
       "Grumpier Old Men (1995)                   0.180048                   0.000000   \n",
       "Waiting to Exhale (1995)                  1.000000                   0.000000   \n",
       "Father of the Bride Part II (1995)        0.453064                   0.000000   \n",
       "...                                            ...                        ...   \n",
       "Meet the Parents (2000)                   0.453064                   0.000000   \n",
       "Requiem for a Dream (2000)                0.392756                   0.000000   \n",
       "Tigerland (2000)                          0.392756                   0.000000   \n",
       "Two Family House (2000)                   0.392756                   0.000000   \n",
       "Contender, The (2000)                     0.127511                   0.000000   \n",
       "\n",
       "title                               Get Carter (2000)  Get Carter (1971)  \\\n",
       "title                                                                      \n",
       "Toy Story (1995)                             0.000000           0.000000   \n",
       "Jumanji (1995)                               0.000000           0.000000   \n",
       "Grumpier Old Men (1995)                      0.000000           0.000000   \n",
       "Waiting to Exhale (1995)                     0.066577           0.000000   \n",
       "Father of the Bride Part II (1995)           0.000000           0.000000   \n",
       "...                                               ...                ...   \n",
       "Meet the Parents (2000)                      0.000000           0.000000   \n",
       "Requiem for a Dream (2000)                   0.169513           0.000000   \n",
       "Tigerland (2000)                             0.169513           0.000000   \n",
       "Two Family House (2000)                      0.169513           0.000000   \n",
       "Contender, The (2000)                        0.522128           0.527915   \n",
       "\n",
       "title                               Meet the Parents (2000)  \\\n",
       "title                                                         \n",
       "Toy Story (1995)                                   0.171093   \n",
       "Jumanji (1995)                                     0.000000   \n",
       "Grumpier Old Men (1995)                            0.397402   \n",
       "Waiting to Exhale (1995)                           0.453064   \n",
       "Father of the Bride Part II (1995)                 1.000000   \n",
       "...                                                     ...   \n",
       "Meet the Parents (2000)                            1.000000   \n",
       "Requiem for a Dream (2000)                         0.000000   \n",
       "Tigerland (2000)                                   0.000000   \n",
       "Two Family House (2000)                            0.000000   \n",
       "Contender, The (2000)                              0.000000   \n",
       "\n",
       "title                               Requiem for a Dream (2000)  \\\n",
       "title                                                            \n",
       "Toy Story (1995)                                      0.000000   \n",
       "Jumanji (1995)                                        0.000000   \n",
       "Grumpier Old Men (1995)                               0.000000   \n",
       "Waiting to Exhale (1995)                              0.392756   \n",
       "Father of the Bride Part II (1995)                    0.000000   \n",
       "...                                                        ...   \n",
       "Meet the Parents (2000)                               0.000000   \n",
       "Requiem for a Dream (2000)                            1.000000   \n",
       "Tigerland (2000)                                      1.000000   \n",
       "Two Family House (2000)                               1.000000   \n",
       "Contender, The (2000)                                 0.324657   \n",
       "\n",
       "title                               Tigerland (2000)  Two Family House (2000)  \\\n",
       "title                                                                           \n",
       "Toy Story (1995)                            0.000000                 0.000000   \n",
       "Jumanji (1995)                              0.000000                 0.000000   \n",
       "Grumpier Old Men (1995)                     0.000000                 0.000000   \n",
       "Waiting to Exhale (1995)                    0.392756                 0.392756   \n",
       "Father of the Bride Part II (1995)          0.000000                 0.000000   \n",
       "...                                              ...                      ...   \n",
       "Meet the Parents (2000)                     0.000000                 0.000000   \n",
       "Requiem for a Dream (2000)                  1.000000                 1.000000   \n",
       "Tigerland (2000)                            1.000000                 1.000000   \n",
       "Two Family House (2000)                     1.000000                 1.000000   \n",
       "Contender, The (2000)                       0.324657                 0.324657   \n",
       "\n",
       "title                               Contender, The (2000)  \n",
       "title                                                      \n",
       "Toy Story (1995)                                 0.000000  \n",
       "Jumanji (1995)                                   0.000000  \n",
       "Grumpier Old Men (1995)                          0.000000  \n",
       "Waiting to Exhale (1995)                         0.127511  \n",
       "Father of the Bride Part II (1995)               0.000000  \n",
       "...                                                   ...  \n",
       "Meet the Parents (2000)                          0.000000  \n",
       "Requiem for a Dream (2000)                       0.324657  \n",
       "Tigerland (2000)                                 0.324657  \n",
       "Two Family House (2000)                          0.324657  \n",
       "Contender, The (2000)                            1.000000  \n",
       "\n",
       "[3883 rows x 3883 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4ba6a30-6748-4c3a-96a0-9f2dd5ab350b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3883, 3883)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ed6334df-3297-4fd3-8617-b13bc995b72d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Toy Story (1995)', 'Jumanji (1995)', 'Grumpier Old Men (1995)',\n",
       "       'Waiting to Exhale (1995)', 'Father of the Bride Part II (1995)',\n",
       "       'Heat (1995)', 'Sabrina (1995)', 'Tom and Huck (1995)',\n",
       "       'Sudden Death (1995)', 'GoldenEye (1995)',\n",
       "       ...\n",
       "       'Bamboozled (2000)', 'Bootmen (2000)', 'Digimon: The Movie (2000)',\n",
       "       'Get Carter (2000)', 'Get Carter (1971)', 'Meet the Parents (2000)',\n",
       "       'Requiem for a Dream (2000)', 'Tigerland (2000)',\n",
       "       'Two Family House (2000)', 'Contender, The (2000)'],\n",
       "      dtype='object', name='title', length=3883)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bee0a1fa-8f4c-4c35-b776-c377e0cf3946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Toy Story (1995)', 'Jumanji (1995)', 'Grumpier Old Men (1995)',\n",
       "       'Waiting to Exhale (1995)', 'Father of the Bride Part II (1995)',\n",
       "       'Heat (1995)', 'Sabrina (1995)', 'Tom and Huck (1995)',\n",
       "       'Sudden Death (1995)', 'GoldenEye (1995)',\n",
       "       ...\n",
       "       'Bamboozled (2000)', 'Bootmen (2000)', 'Digimon: The Movie (2000)',\n",
       "       'Get Carter (2000)', 'Get Carter (1971)', 'Meet the Parents (2000)',\n",
       "       'Requiem for a Dream (2000)', 'Tigerland (2000)',\n",
       "       'Two Family House (2000)', 'Contender, The (2000)'],\n",
       "      dtype='object', name='title', length=3883)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bda82d9f-d3e5-428b-838c-61613b64fbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use random number generator to create training and testing dataset.\n",
    "def create_test_movie_ratings(x):\n",
    "    if pd.isna(x):\n",
    "        return np.nan\n",
    "    else:\n",
    "        if np.random.rand() < 0.8:\n",
    "            return x\n",
    "        else:\n",
    "            return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "292a536a-0bc3-4667-abf3-5eddc15c12f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_movie_ratings = actual_movie_ratings.applymap(create_test_movie_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9b459ab-064f-4ef7-a1eb-1d8259853910",
   "metadata": {},
   "outputs": [
    {
     "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>6031</th>\n",
       "      <th>6032</th>\n",
       "      <th>6033</th>\n",
       "      <th>6034</th>\n",
       "      <th>6035</th>\n",
       "      <th>6036</th>\n",
       "      <th>6037</th>\n",
       "      <th>6038</th>\n",
       "      <th>6039</th>\n",
       "      <th>6040</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>5.0</td>\n",
       "      <td>...</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>3</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>3949</th>\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>...</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>3950</th>\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>...</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>3951</th>\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>...</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>3952</th>\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>...</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",
       "<p>3706 rows × 6040 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id   1     2     3     4     5     6     7     8     9     10    ...  \\\n",
       "movie_id                                                              ...   \n",
       "1          NaN   NaN   NaN   NaN   NaN   4.0   NaN   4.0   5.0   5.0  ...   \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0  ...   \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN  ...   \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "user_id   6031  6032  6033  6034  6035  6036  6037  6038  6039  6040  \n",
       "movie_id                                                              \n",
       "1          NaN   4.0   NaN   NaN   4.0   NaN   NaN   NaN   NaN   3.0  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   2.0   2.0   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[3706 rows x 6040 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_movie_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0d7bf0bd-b66f-4464-bced-e0bfb056d082",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_movie_ratings = test_movie_ratings.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a8c16e2-21cd-4f86-b554-cb78fea95bdc",
   "metadata": {},
   "outputs": [
    {
     "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>6031</th>\n",
       "      <th>6032</th>\n",
       "      <th>6033</th>\n",
       "      <th>6034</th>\n",
       "      <th>6035</th>\n",
       "      <th>6036</th>\n",
       "      <th>6037</th>\n",
       "      <th>6038</th>\n",
       "      <th>6039</th>\n",
       "      <th>6040</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>5.0</td>\n",
       "      <td>...</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>3</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>3949</th>\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>...</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>3950</th>\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>...</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>3951</th>\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>...</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>3952</th>\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>...</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",
       "<p>3706 rows × 6040 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id   1     2     3     4     5     6     7     8     9     10    ...  \\\n",
       "movie_id                                                              ...   \n",
       "1          NaN   NaN   NaN   NaN   NaN   4.0   NaN   4.0   5.0   5.0  ...   \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0  ...   \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN  ...   \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "user_id   6031  6032  6033  6034  6035  6036  6037  6038  6039  6040  \n",
       "movie_id                                                              \n",
       "1          NaN   4.0   NaN   NaN   4.0   NaN   NaN   NaN   NaN   3.0  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   2.0   2.0   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[3706 rows x 6040 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_movie_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bfef394f-3b49-403a-95ed-e02132f8ae11",
   "metadata": {},
   "outputs": [
    {
     "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>user_id</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>6031</th>\n",
       "      <th>6032</th>\n",
       "      <th>6033</th>\n",
       "      <th>6034</th>\n",
       "      <th>6035</th>\n",
       "      <th>6036</th>\n",
       "      <th>6037</th>\n",
       "      <th>6038</th>\n",
       "      <th>6039</th>\n",
       "      <th>6040</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_id</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>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>5.0</td>\n",
       "      <td>...</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>3</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3948</th>\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>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</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>3949</th>\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>...</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>3950</th>\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>...</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>3951</th>\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>...</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>3952</th>\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>...</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",
       "<p>3706 rows × 6040 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id   1     2     3     4     5     6     7     8     9     10    ...  \\\n",
       "movie_id                                                              ...   \n",
       "1          5.0   NaN   NaN   NaN   NaN   4.0   NaN   4.0   5.0   5.0  ...   \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   5.0  ...   \n",
       "3          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "4          NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   NaN   NaN  ...   \n",
       "5          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   3.0   4.0  ...   \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...   \n",
       "\n",
       "user_id   6031  6032  6033  6034  6035  6036  6037  6038  6039  6040  \n",
       "movie_id                                                              \n",
       "1          NaN   4.0   NaN   NaN   4.0   NaN   NaN   NaN   NaN   3.0  \n",
       "2          NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "4          NaN   NaN   NaN   NaN   2.0   2.0   NaN   NaN   NaN   NaN  \n",
       "5          NaN   NaN   NaN   NaN   1.0   NaN   NaN   NaN   NaN   NaN  \n",
       "...        ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "3948       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3949       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3950       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3951       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "3952       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[3706 rows x 6040 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actual_movie_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7182fd9c-72e4-4162-9a84-fb840ad57275",
   "metadata": {},
   "source": [
    "## Getting top 10 recommended movies list for Toy Story 1 (Children's and Animation Genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d8bfd8b4-a18b-40ee-b35a-9ef09cd32bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def genre_recommendations(i, M, items, k):\n",
    "    \"\"\"\n",
    "    Recommends movies based on a similarity dataframe\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    i : str\n",
    "        Movie (index of the similarity dataframe)\n",
    "    M : pd.DataFrame\n",
    "        Similarity dataframe, symmetric, with movies as indices and columns\n",
    "    items : pd.DataFrame\n",
    "        Contains both the title and some other features used to define similarity\n",
    "    k : int\n",
    "        Amount of recommendations to return\n",
    "\n",
    "    \"\"\"\n",
    "    ix = M.loc[:,i].to_numpy().argpartition(range(-1,-k,-1))\n",
    "    closest = M.columns[ix[-1:-(k+2):-1]]\n",
    "    closest = closest.drop(i, errors='ignore')\n",
    "    return pd.DataFrame(closest, columns=[\"title\"]).head(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6538e7dc-6ca6-45a8-8057-6f99f1e40f3a",
   "metadata": {},
   "outputs": [
    {
     "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</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>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id             title                        genre\n",
       "0         1  Toy Story (1995)  Animation|Children's|Comedy"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie[df_movie['title'].eq('Toy Story (1995)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5505b06c-142e-46af-80f9-7324b7df001a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_list = genre_recommendations('Toy Story (1995)', cosine_sim_df, df_movie[['title', 'genre']], k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e83054f1-72af-4202-8f3d-cb5dd18a3616",
   "metadata": {},
   "outputs": [
    {
     "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>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aladdin and the King of Thieves (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Tail: Fievel Goes West, An (1991)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>American Tail, An (1986)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rugrats Movie, The (1998)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saludos Amigos (1943)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adventures of Rocky and Bullwinkle, The (2000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chicken Run (2000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Steamboat Willie (1940)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title\n",
       "0          Aladdin and the King of Thieves (1996)\n",
       "1                            Bug's Life, A (1998)\n",
       "2                              Toy Story 2 (1999)\n",
       "3      American Tail: Fievel Goes West, An (1991)\n",
       "4                        American Tail, An (1986)\n",
       "5                       Rugrats Movie, The (1998)\n",
       "6                           Saludos Amigos (1943)\n",
       "7  Adventures of Rocky and Bullwinkle, The (2000)\n",
       "8                              Chicken Run (2000)\n",
       "9                         Steamboat Willie (1940)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e1451a0c-5338-4f4e-bc97-c09631d9fcdb",
   "metadata": {},
   "outputs": [
    {
     "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>2033</td>\n",
       "      <td>Black Cauldron, The (1985)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                       title                 genre\n",
       "1964      2033  Black Cauldron, The (1985)  Animation|Children's"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie[df_movie['title'].eq('Black Cauldron, The (1985)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4b2adcd8-0fbc-4f7d-9b2b-6f4215fafcc7",
   "metadata": {},
   "outputs": [
    {
     "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</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>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id             title                        genre\n",
       "0         1  Toy Story (1995)  Animation|Children's|Comedy"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie[df_movie['title'].eq('Toy Story (1995)')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb752dc3-fc4e-41f0-af21-3074c9adf905",
   "metadata": {},
   "source": [
    "## Finding all the missing ratings in testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0ad0ca24-5eff-4177-8083-c4cbad039a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores1 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b434df9d-8236-4b97-9bba-ba708a805125",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3706/3706 [6:25:57<00:00,  6.25s/it]   \n"
     ]
    }
   ],
   "source": [
    "# Compute recommendations for each user/movie pair\n",
    "for movie_id in tqdm(test_movie_ratings.index):\n",
    "    title = id_to_title[movie_id]\n",
    "    rec_df = rec_scores[title]\n",
    "    for user_id in test_movie_ratings.columns:\n",
    "        if pd.isna(test_movie_ratings.loc[movie_id, user_id]) and not pd.isna(actual_movie_ratings.loc[movie_id, user_id]):\n",
    "            ratings = []\n",
    "            similarities = []\n",
    "            rating_similarity_pair = []\n",
    "            for t, s in rec_df.items():\n",
    "                m_id = df_movie.loc[df_movie[\"title\"] == t, 'movie_id'].iloc[0]\n",
    "                if (m_id not in train_movie_ratings.index):\n",
    "                    continue\n",
    "                rating = train_movie_ratings.loc[m_id, user_id]\n",
    "                if(not pd.isna(rating)):\n",
    "                    ratings.append(rating)\n",
    "                    similarities.append(s)\n",
    "                    rating_similarity_pair.append((rating, s))\n",
    "                if len(ratings) == 5:\n",
    "                    break\n",
    "            numerator = np.dot(np.array([pair_[0] for pair_ in rating_similarity_pair]), np.array([pair_[1] for pair_ in rating_similarity_pair]))\n",
    "            denominator = sum(similarities)\n",
    "            if (denominator > 0):\n",
    "                predicted = numerator / denominator\n",
    "                actual = actual_movie_ratings.loc[movie_id, user_id]\n",
    "                scores1.append((predicted, actual))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b3a0e931-d7f2-4ff2-b205-583aa8972e73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(3.0542830816719904, 3.0), (4.5169491875935215, 5.0), (3.814022927141504, 5.0), (3.6, 4.0), (3.286181085887839, 4.0), (3.5575083175067106, 3.0), (4.432469065005471, 5.0), (4.064462711273508, 5.0), (3.9466300298363257, 4.0), (3.87696939832057, 2.0), (4.339350407025609, 5.0), (4.382779183185253, 5.0), (4.00135631926444, 4.0), (4.463157753854373, 5.0), (4.081172662513678, 5.0), (3.9625615138243573, 5.0), (3.2, 5.0), (5.000000000000001, 5.0), (3.600000000000001, 4.0), (3.3518788334460887, 5.0), (3.9203433399989143, 5.0), (4.566192163171453, 4.0), (3.5855176768849426, 4.0), (4.155084679261766, 4.0), (2.418588145099533, 4.0), (4.29880370629489, 4.0), (4.258743600454094, 5.0), (3.3079903347704196, 4.0), (4.012624093991773, 5.0), (4.764110410883258, 5.0), (4.216925508335834, 5.0), (4.468039290722686, 5.0), (2.3345743180952043, 2.0), (3.536097900105294, 4.0), (3.57660985564639, 3.0), (4.216234532502735, 5.0), (3.2434693661665883, 4.0), (4.088343760793781, 4.0), (4.672333174436268, 5.0), (4.546837615512812, 4.0), (4.471779178233485, 5.0), (4.18094346231114, 5.0), (4.364444966657279, 4.0), (3.0013266982174422, 5.0), (3.0816309830584365, 3.0), (2.9199709262130242, 3.0), (4.462759527927536, 5.0), (3.755888069944728, 3.0), (4.199174575748964, 5.0), (4.4971741588749525, 3.0), (3.4, 4.0), (3.0, 4.0), (3.4890391015103037, 5.0), (4.379945415363022, 5.0), (4.370460088450127, 3.0), (4.999999999999999, 5.0), (3.617147799761481, 4.0), (4.28084169290688, 4.0), (3.7835327156175182, 4.0), (3.0618455903036983, 4.0), (4.6000000000000005, 5.0), (3.528574561543959, 4.0), (3.6080660058172382, 4.0), (3.4781487624054153, 5.0), (4.560116521779758, 4.0), (4.130079883168832, 4.0), (4.699725743071917, 4.0), (2.617220816814747, 2.0), (4.676450986374255, 4.0), (3.3481156600623425, 4.0), (3.856910353559404, 4.0), (3.52482230439654, 4.0), (2.5511553895969636, 3.0), (3.589561240167911, 4.0), (3.4733448495790387, 4.0), (3.9019184744935327, 5.0), (3.559605418926682, 4.0), (4.216234532502735, 5.0), (4.216234532502735, 4.0), (4.432469065005471, 5.0), (4.1083304273081165, 4.0), (3.3717671958953748, 3.0), (3.058718696444403, 3.0), (4.463974269106259, 5.0), (4.419173436104475, 5.0), (4.3008392826860184, 4.0), (3.6947732700117553, 4.0), (3.6, 4.0), (4.0, 3.0), (3.567530934994529, 5.0), (3.8275308779845183, 5.0), (4.643328574263097, 4.0), (4.273320786231736, 4.0), (4.0, 4.0), (4.1469937435237325, 5.0), (4.172832970475883, 5.0), (3.4, 5.0), (3.0, 4.0), (4.098441024858259, 4.0), (3.578868811536926, 3.0)]\n"
     ]
    }
   ],
   "source": [
    "print(scores1[:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9fdce3a-7aab-44f0-a96d-2529663ce963",
   "metadata": {},
   "source": [
    "## Calculating RMSE and MAE based on scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d226edb8-2d88-4b8c-ba31-436027ce4827",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7c62cd7e-7456-496e-83f2-acb571ec9412",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rmse_and_mae(predictions):\n",
    "    n = len(predictions)\n",
    "    rmse = math.sqrt(sum([(p[0]-p[1])**2 for p in predictions])/n)\n",
    "    mae = sum([abs(p[0]-p[1]) for p in predictions])/n\n",
    "    return rmse, mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "87536ade-1742-4e55-bcfa-4bed95564721",
   "metadata": {},
   "outputs": [],
   "source": [
    "rsme, mae = calculate_rmse_and_mae(scores1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ac28a12-4b28-4e21-864e-3e3a93785616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSME:  1.0773752483796206\n",
      "MAE:  0.8457910745108963\n"
     ]
    }
   ],
   "source": [
    "print(\"RSME: \", rsme)\n",
    "print(\"MAE: \", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed19306f-c352-409b-bc5f-341cd3998741",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "\n",
    "### In conclusion, our content based collaborative filtering yielded a score of 1.08 RMSE and 0.84 MAE which are worse than the item-to-item and user-to-user collaborative filtering"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}