Movie_Recommendation_System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1568d57d",
   "metadata": {},
   "source": [
    "# A Movie Recommendation System in Python from Scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac2c0d9",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fda85cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4ba5e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import NearestNeighbors \n",
    "from scipy.sparse import csr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "760defd0",
   "metadata": {},
   "source": [
    "### Loading the data from Amazon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9c398c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rating dataset\n",
    "ratings = pd.read_csv(\"https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv\")\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ab3b5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# movie dataset\n",
    "movies = pd.read_csv(\"https://s3-us-west-2.amazonaws.com/recommender-tutorial/movies.csv\")\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c68eb98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique movieId's: 9724\n",
      "Number of unique users: 610\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of unique movieId's: {len(ratings['movieId'].unique())}\")\n",
    "print(f\"Number of unique users: {len(ratings['userId'].unique())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9951743c",
   "metadata": {},
   "source": [
    "### EDA: User Rating Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be3334ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>n_ratings</th>\n",
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      "text/plain": [
       "   userId  n_ratings\n",
       "0       1        232\n",
       "1       2         29\n",
       "2       3         39\n",
       "3       4        216\n",
       "4       5         44"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_freq = ratings[['userId','rating']].groupby(['userId']).count().reset_index()\n",
    "user_freq.columns = ['userId', 'n_ratings']\n",
    "user_freq.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2886450",
   "metadata": {},
   "outputs": [
    {
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       "      <th>413</th>\n",
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      "text/plain": [
       "     userId  n_ratings\n",
       "413     414       2698"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "user_freq.query(' n_ratings.max() == n_ratings ').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9d431f1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>n_ratings</th>\n",
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      "text/plain": [
       "     userId  n_ratings\n",
       "52       53         20\n",
       "146     147         20\n",
       "188     189         20\n",
       "193     194         20\n",
       "206     207         20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_freq.query(' n_ratings.min() == n_ratings ').head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb998892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(user_freq['n_ratings'])\n",
    "plt.xlabel('n_ratings')\n",
    "plt.ylabel('counts')\n",
    "plt.title('User Rating Frequency')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9bb855",
   "metadata": {},
   "source": [
    "## Collaborative Filtering System\n",
    "\n",
    "Collaborative Filtering recommends items based on similarity measures between users and/or items. The basic assumption behind the algorithm is that users with similar interests have common preferences.\n",
    "\n",
    "Collaborative filtering uses a **user-item** matrix to generate recommendations. This matrix contains the values that indicate a user’s preference towards a given item. These values can represent either explicit feedback (direct user ratings) or implicit feedback (indirect user behavior such as listening, purchasing, watching).\n",
    "\n",
    "\n",
    "### User-Item Matrix Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba1a2964",
   "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>userId</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>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </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>193581</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193583</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193585</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193587</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193609</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9724 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  ...  4.0  0.0  4.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "4        0.0  0.0  0.0  0.0  0.0  3.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  0.0  0.0  2.0  0.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  2.0  0.0  0.0  \n",
       "4        0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "193581   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193583   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193585   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193587   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "193609   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[9724 rows x 610 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.pivot(ratings, index='movieId', columns='userId', values='rating').fillna(0)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0e46f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mappers to be able to map an index of the matrix X back to a movie or user ID\n",
    "user_index_to_id  = {i:j for i,j in enumerate(X.columns)}\n",
    "movie_index_to_id = {i:j for i,j in enumerate(X.index)}\n",
    "\n",
    "user_id_to_index  = {j:i for i,j in enumerate(X.columns)}\n",
    "movie_id_to_index = {j:i for i,j in enumerate(X.index)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd6d236",
   "metadata": {},
   "source": [
    "## Main recommendation code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "648f6b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommendation_movie_system(target_movie_id, X, k, metric='cosine'):\n",
    "    \n",
    "    # we will store the recommendations in a list named `recommendations`\n",
    "    recommendations = []\n",
    "    \n",
    "    # Map movie_id to movie index to slice matrix X \n",
    "    movie_ind = movie_id_to_index[target_movie_id] # this is an index now\n",
    "    movie_vec = X.values[movie_ind].reshape(1,-1) # this is a vector of shape [1, 610] for the given movie id\n",
    "\n",
    "    # refine the Uunsupervised learner for implementing neighbor searches\n",
    "    knn = NearestNeighbors(n_neighbors=k+1, metric=metric).fit(X)\n",
    "    \n",
    "    _ , neigh_ind = knn.kneighbors(movie_vec)\n",
    "    recommendations = [movie_index_to_id[neigh_ind[0][i]] for i in range(k+1)]\n",
    "    \n",
    "    # we will pop the first element as this is the same item as the target_movie_id\n",
    "    recommendations.pop(0)\n",
    "    \n",
    "    return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ea32e2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The title of the target movie id is Toy Story (1995)\n"
     ]
    }
   ],
   "source": [
    "# Target movie ID in order to find similar movies\n",
    "target_movie_id = 1\n",
    "print(f\"The title of the target movie id is {movies.query( 'movieId == @target_movie_id' ).title.values[0]}\")\n",
    "\n",
    "get_n_recommendations = 5\n",
    "\n",
    "similar_movie_ids = recommendation_movie_system(target_movie_id, X, k=get_n_recommendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "65fca9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Toy Story 2 (1999)',\n",
       " 'Jurassic Park (1993)',\n",
       " 'Independence Day (a.k.a. ID4) (1996)',\n",
       " 'Star Wars: Episode IV - A New Hope (1977)',\n",
       " 'Forrest Gump (1994)']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mapper of movie id to movie title\n",
    "movie_titles = dict(zip(movies.movieId, movies.title))\n",
    "\n",
    "# extract the movie titles of the estimated recommendations\n",
    "[movie_titles[i] for i in similar_movie_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7669d9",
   "metadata": {},
   "source": [
    "## Movie Recommendation with respect to Users Preference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da395ac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_for_user(target_user_id, X, movie_titles, k=get_n_recommendations):\n",
    "    \n",
    "    # get the ratings of this target user\n",
    "    user_ratings = ratings.query(' userId == @target_user_id ')\n",
    "    \n",
    "    # find the movie id with max rating from this user. If many, get the first found\n",
    "    max_rated_by_target_user = user_ratings.query(' rating == rating.max() ').iloc[0]\n",
    "    max_movie_id = max_rated_by_target_user['movieId']\n",
    "    max_movie_title = movie_titles[max_movie_id]\n",
    "    \n",
    "    # get recommendations based on the target_movie_id\n",
    "    similar_movie_ids = recommendation_movie_system(max_movie_id, X, k=k)\n",
    "    \n",
    "    print(f\"Since you liked a lot {max_movie_title}, you might also like:\")\n",
    "    print(f\"\\n\")\n",
    "\n",
    "    for movie_ in similar_movie_ids:\n",
    "        print(movie_titles.get(movie_, \"Movie not found\"))\n",
    "    \n",
    "    return max_movie_id, max_movie_title, similar_movie_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "569f0dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# our target user\n",
    "target_user_id = 150 \n",
    "\n",
    "# how many movie recommendations to get\n",
    "get_n_recommendations = 5\n",
    "\n",
    "# run the recommendation system for this user\n",
    "_ ,_ ,_ = recommend_for_user(target_user_id, X, movie_titles, k=get_n_recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970b0c01",
   "metadata": {},
   "source": [
    "# the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d8c2982",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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