Explicit Collaborative filtering using truncated SVD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22823e06",
   "metadata": {},
   "source": [
    "In this notebook I am gonna do Explicit Collaborative Filtering using ratings data so as to recommend movies to the user based on his previous likings in the past and also based on similar user rating that movie.After the end of this notebook,you will\n",
    "1. recommend movies based on movie-movie interaction and finding the similar movies to a movie.\\\n",
    "2.predicting movies that user did not rate and finding top k to recommend\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba720293",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Installing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "903df9bc",
   "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>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:53:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:31:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:33:39</td>\n",
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       "      <td>2005-04-02 23:32:07</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2005-04-02 23:29:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000258</th>\n",
       "      <td>138493</td>\n",
       "      <td>68954</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-11-13 15:42:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000259</th>\n",
       "      <td>138493</td>\n",
       "      <td>69526</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2009-12-03 18:31:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000260</th>\n",
       "      <td>138493</td>\n",
       "      <td>69644</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009-12-07 18:10:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000261</th>\n",
       "      <td>138493</td>\n",
       "      <td>70286</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2009-11-13 15:42:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000262</th>\n",
       "      <td>138493</td>\n",
       "      <td>71619</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2009-10-17 20:25:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating            timestamp\n",
       "0              1        2     3.5  2005-04-02 23:53:47\n",
       "1              1       29     3.5  2005-04-02 23:31:16\n",
       "2              1       32     3.5  2005-04-02 23:33:39\n",
       "3              1       47     3.5  2005-04-02 23:32:07\n",
       "4              1       50     3.5  2005-04-02 23:29:40\n",
       "...          ...      ...     ...                  ...\n",
       "20000258  138493    68954     4.5  2009-11-13 15:42:00\n",
       "20000259  138493    69526     4.5  2009-12-03 18:31:48\n",
       "20000260  138493    69644     3.0  2009-12-07 18:10:57\n",
       "20000261  138493    70286     5.0  2009-11-13 15:42:24\n",
       "20000262  138493    71619     2.5  2009-10-17 20:25:36\n",
       "\n",
       "[20000263 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv('rating.csv')\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d3fb0636",
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27278 rows × 3 columns</p>\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",
       "27273   131254        Kein Bund für's Leben (2007)   \n",
       "27274   131256       Feuer, Eis & Dosenbier (2002)   \n",
       "27275   131258                  The Pirates (2014)   \n",
       "27276   131260                 Rentun Ruusu (2001)   \n",
       "27277   131262                    Innocence (2014)   \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  \n",
       "...                                            ...  \n",
       "27273                                       Comedy  \n",
       "27274                                       Comedy  \n",
       "27275                                    Adventure  \n",
       "27276                           (no genres listed)  \n",
       "27277                     Adventure|Fantasy|Horror  \n",
       "\n",
       "[27278 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('movie.csv')\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401d3d0e",
   "metadata": {},
   "source": [
    "To make the memory manageable we will use only 45 percentage of entire 20 million dataset. So, lets randomly select some users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cd781ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9019314 rows of data from 62321 users\n"
     ]
    }
   ],
   "source": [
    "rand_userIds = np.random.choice(ratings['userId'].unique(), \n",
    "                                size=int(len(ratings['userId'].unique())*0.45), \n",
    "                                replace=False)\n",
    "\n",
    "ratings = ratings.loc[ratings['userId'].isin(rand_userIds)]\n",
    "\n",
    "print('There are {} rows of data from {} users'.format(len(ratings), len(rand_userIds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1096cd7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = ratings[['userId', 'movieId', 'rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c3b2b7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have 9019314 ratings\n",
      "the number of unique users we have is: 62321\n",
      "the number of unique movies we have is: 23368\n",
      "The median user rated 68 movies.\n",
      "The max rating is: 5 the min rating is: 0\n"
     ]
    }
   ],
   "source": [
    "print('we have',train.shape[0], 'ratings')\n",
    "print('the number of unique users we have is:', len(train.userId.unique()))\n",
    "print('the number of unique movies we have is:', len(train.movieId.unique()))\n",
    "print(\"The median user rated %d movies.\"%train.userId.value_counts().median())\n",
    "print('The max rating is: %d'%train.rating.max(),\"the min rating is: %d\"%train.rating.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c238c9db",
   "metadata": {},
   "source": [
    "Exploratory Data Analysis\n",
    "\n",
    "Lets find the distribution of movie ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9521f3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"rating\", data=ratings, palette=\"viridis\")\n",
    "plt.title(\"Distribution of movie ratings\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd0f49a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_X(df):\n",
    "    \"\"\"\n",
    "    Generates a sparse matrix from ratings dataframe.\n",
    "\n",
    "    Args:\n",
    "        df: pandas dataframe containing 3 columns (userId, movieId, rating)\n",
    "\n",
    "    Returns:\n",
    "        X: sparse matrix\n",
    "        user_mapper: dict that maps user id's to user indices\n",
    "        user_inv_mapper: dict that maps user indices to user id's\n",
    "        movie_mapper: dict that maps movie id's to movie indices\n",
    "        movie_inv_mapper: dict that maps movie indices to movie id's\n",
    "        \n",
    "      We need these dictionaries because they map which row/column of the utility matrix corresponds to which user/movie id.\n",
    "\n",
    "    Our X (user-item) matrix is a scipy.sparse.csr_matrix which stores the data sparsely.\n",
    "    \"\"\"\n",
    "    M = df['userId'].nunique()\n",
    "    N = df['movieId'].nunique()\n",
    "\n",
    "    user_mapper = dict(zip(np.unique(df[\"userId\"]), list(range(M))))\n",
    "    movie_mapper = dict(zip(np.unique(df[\"movieId\"]), list(range(N))))\n",
    "\n",
    "    user_inv_mapper = dict(zip(list(range(M)), np.unique(df[\"userId\"])))\n",
    "    movie_inv_mapper = dict(zip(list(range(N)), np.unique(df[\"movieId\"])))\n",
    "\n",
    "    user_index = [user_mapper[i] for i in df['userId']]\n",
    "    item_index = [movie_mapper[i] for i in df['movieId']]\n",
    "\n",
    "    X = csr_matrix((df[\"rating\"], (user_index,item_index)), shape=(M,N))\n",
    "\n",
    "    return X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper\n",
    "\n",
    "X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_X(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2b42f7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix sparsity: 0.62%\n"
     ]
    }
   ],
   "source": [
    "n_total = X.shape[0]*X.shape[1]\n",
    "n_ratings = X.nnz\n",
    "sparsity = n_ratings/n_total\n",
    "print(f\"Matrix sparsity: {round(sparsity*100,2)}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17c5d703",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(62321, 23368)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3c74aab",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.merge(movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7b9a5620",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23368, 30)\n"
     ]
    }
   ],
   "source": [
    "# initial hyperparameter\n",
    "epsilon = 1e-9\n",
    "n_latent_factors = 30\n",
    "\n",
    "# generate item latent features\n",
    "item_svd = TruncatedSVD(n_components = n_latent_factors)\n",
    "item_features = item_svd.fit_transform(X.transpose()) + epsilon\n",
    "print(np.shape(item_features))\n",
    "\n",
    "# generate user latent features\n",
    "user_svd = TruncatedSVD(n_components = n_latent_factors)\n",
    "user_features = user_svd.fit_transform(X) + epsilon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db0ca725",
   "metadata": {},
   "source": [
    "TRUNCATED SINGULAR VALUE DECOMPOSITION\n",
    "\n",
    "There can be some generalities in our data more concise and descriptive rather than 60k rows. For instance, any given movie can, to a rough degree of approximation, be described in terms of some basic attributes such as overall quality, whether it's an action movie or a comedy, what stars are in it, and so on. And every user's preferences can likewise be roughly described in terms of whether they tend to rate high or low, whether they prefer action movies or comedies, what stars they like, and so on. And if those basic assumptions are true, then a lot of the 60 thousand ratings ought to be explainable by a lot less than 60 k  numbers, since, for instance, a single number specifying how much action a particular movie has may help explain why a few million action-buffs like that movie.\n",
    "\n",
    "So,now the problem arises how to get those attributes(latent factors) to our utility matrix(user-movie ratings matrix). We can get this by utilising the mathematical concept of Truncated Singular Value Decomposition(SVD) and decompose it to smaller components.\n",
    "SVD works by decomposing a matrix into three matrices. \n",
    "\n",
    "A = U.S.V.T\n",
    "\n",
    "Matrix U: singular matrix of (user*latent factors)\n",
    "\n",
    "Matrix S: diagonal matrix (shows the strength of each latent factor)\n",
    "\n",
    "Matrix V: singular matrix of (item*latent factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65b35cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_movies(movie_id, X, movie_mapper, movie_inv_mapper, k, metric='cosine'):\n",
    "    \"\"\"\n",
    "    Finds k-nearest neighbours for a given movie id.(used to predict similar movies to a movie)\n",
    "\n",
    "    Args:\n",
    "        movie_id: id of the movie of interest\n",
    "        X: user-item utility matrix\n",
    "        k: number of similar movies to retrieve\n",
    "        metric: distance metric for kNN calculations\n",
    "\n",
    "    Output: returns list of k similar movie ID's\n",
    "    \"\"\"\n",
    "    X = X.T\n",
    "    neighbour_ids = []\n",
    "\n",
    "    movie_ind = movie_mapper[movie_id]\n",
    "    movie_vec = X[movie_ind]\n",
    "    if isinstance(movie_vec, (np.ndarray)):\n",
    "        movie_vec = movie_vec.reshape(1,-1)\n",
    "    # use k+1 since kNN output includes the movieId of interest\n",
    "    kNN = NearestNeighbors(n_neighbors=k+1, algorithm=\"brute\", metric=metric)\n",
    "    kNN.fit(X)\n",
    "    neighbour = kNN.kneighbors(movie_vec, return_distance=False)\n",
    "    for i in range(0,k):\n",
    "        n = neighbour.item(i)\n",
    "        neighbour_ids.append(movie_inv_mapper[n])\n",
    "    neighbour_ids.pop(0)\n",
    "    return neighbour_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ff1357e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019309</th>\n",
       "      <td>138406</td>\n",
       "      <td>84092</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Decision at Sundown (1957)</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019310</th>\n",
       "      <td>138406</td>\n",
       "      <td>108342</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Monkey in Winter, A (Un singe en hiver) (1962)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019311</th>\n",
       "      <td>138406</td>\n",
       "      <td>110167</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Judge and the Assassin, The (Juge et l'assassi...</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019312</th>\n",
       "      <td>138406</td>\n",
       "      <td>110169</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Les Lyonnais (2011)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019313</th>\n",
       "      <td>138406</td>\n",
       "      <td>110510</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Série noire (1979)</td>\n",
       "      <td>Film-Noir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9019314 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         userId  movieId  rating  \\\n",
       "0             2        3     4.0   \n",
       "1             6        3     3.0   \n",
       "2             8        3     5.0   \n",
       "3            19        3     4.0   \n",
       "4            80        3     3.0   \n",
       "...         ...      ...     ...   \n",
       "9019309  138406    84092     3.5   \n",
       "9019310  138406   108342     4.0   \n",
       "9019311  138406   110167     4.5   \n",
       "9019312  138406   110169     4.0   \n",
       "9019313  138406   110510     4.5   \n",
       "\n",
       "                                                     title          genres  \n",
       "0                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "1                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "2                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "3                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "4                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "...                                                    ...             ...  \n",
       "9019309                         Decision at Sundown (1957)         Western  \n",
       "9019310     Monkey in Winter, A (Un singe en hiver) (1962)    Comedy|Drama  \n",
       "9019311  Judge and the Assassin, The (Juge et l'assassi...     Crime|Drama  \n",
       "9019312                                Les Lyonnais (2011)  Drama|Thriller  \n",
       "9019313                                 Série noire (1979)       Film-Noir  \n",
       "\n",
       "[9019314 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d21f36ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Because you watched Toy Story (1995):\n",
      "Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)\n",
      "Now and Then (1995)\n",
      "City of Lost Children, The (Cité des enfants perdus, La) (1995)\n",
      "Leaving Las Vegas (1995)\n",
      "Othello (1995)\n",
      "Persuasion (1995)\n",
      "Powder (1995)\n",
      "Assassins (1995)\n",
      "Get Shorty (1995)\n"
     ]
    }
   ],
   "source": [
    "movie_id = 1\n",
    "movie_titles = dict(zip(movies['movieId'], movies['title']))\n",
    "similar_movies = find_similar_movies(movie_id, item_features, movie_mapper, movie_inv_mapper, metric='cosine', k=10)\n",
    "movie_title = movie_titles[movie_id]\n",
    "print(f\"Because you watched {movie_title}:\")\n",
    "for i in similar_movies:\n",
    "    print(movie_titles[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f10b0e",
   "metadata": {},
   "source": [
    "The results above are the most similar movies to Toy Story using kNN on our “compressed” movie-factor matrix. We reduced the dimensions down to n_components=30. We can think of each component representing a latent feature such as movie genre."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c7a9ba",
   "metadata": {},
   "source": [
    "Predicting missing ratings to generate top N recommendations\n",
    "With matrix factorization, we can also predict missing ratings in our original user-item matrix. We can do this by reconstructing our matrix by getting the inner product of our user-factor matrix and movie-factor matrix and then predicting the ratings which users did not rate by completing the matrix.\n",
    "We can use the inverse_transform on our movie-factor matrix Z to get our reconstructed user-item matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83a45bb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_X =user_svd.inverse_transform(user_features)#We can construct our utility matrix either using item features or user features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b2a2418b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(62321, 23368)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(new_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ac0eebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "      <td>Comedy|Romance</td>\n",
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       "      <td>Comedy|Romance</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
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       "      <th>4</th>\n",
       "      <td>80</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
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       "      <td>84092</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Decision at Sundown (1957)</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019310</th>\n",
       "      <td>138406</td>\n",
       "      <td>108342</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Monkey in Winter, A (Un singe en hiver) (1962)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019311</th>\n",
       "      <td>138406</td>\n",
       "      <td>110167</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Judge and the Assassin, The (Juge et l'assassi...</td>\n",
       "      <td>Crime|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019312</th>\n",
       "      <td>138406</td>\n",
       "      <td>110169</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Les Lyonnais (2011)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9019313</th>\n",
       "      <td>138406</td>\n",
       "      <td>110510</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Série noire (1979)</td>\n",
       "      <td>Film-Noir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9019314 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         userId  movieId  rating  \\\n",
       "0             2        3     4.0   \n",
       "1             6        3     3.0   \n",
       "2             8        3     5.0   \n",
       "3            19        3     4.0   \n",
       "4            80        3     3.0   \n",
       "...         ...      ...     ...   \n",
       "9019309  138406    84092     3.5   \n",
       "9019310  138406   108342     4.0   \n",
       "9019311  138406   110167     4.5   \n",
       "9019312  138406   110169     4.0   \n",
       "9019313  138406   110510     4.5   \n",
       "\n",
       "                                                     title          genres  \n",
       "0                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "1                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "2                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "3                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "4                                  Grumpier Old Men (1995)  Comedy|Romance  \n",
       "...                                                    ...             ...  \n",
       "9019309                         Decision at Sundown (1957)         Western  \n",
       "9019310     Monkey in Winter, A (Un singe en hiver) (1962)    Comedy|Drama  \n",
       "9019311  Judge and the Assassin, The (Juge et l'assassi...     Crime|Drama  \n",
       "9019312                                Les Lyonnais (2011)  Drama|Thriller  \n",
       "9019313                                 Série noire (1979)       Film-Noir  \n",
       "\n",
       "[9019314 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "66668608",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Recommendations for UserId 19:\n",
      "Independence Day (a.k.a. ID4) (1996)\n",
      "Toy Story (1995)\n",
      "Twister (1996)\n",
      "Rock, The (1996)\n",
      "Mission: Impossible (1996)\n",
      "Willy Wonka & the Chocolate Factory (1971)\n",
      "Fargo (1996)\n",
      "Mr. Holland's Opus (1995)\n",
      "Broken Arrow (1996)\n",
      "Birdcage, The (1996)\n"
     ]
    }
   ],
   "source": [
    "top_N = 10\n",
    "userId = 19\n",
    "movie_titles = dict(zip(movies['movieId'], movies['title']))\n",
    "\n",
    "top_N_indices = new_X[user_mapper[userId]].argsort()[-top_N:][::-1]\n",
    "\n",
    "print(f\"Top {top_N} Recommendations for UserId {userId}:\")\n",
    "for i in top_N_indices:\n",
    "    movie_id = movie_inv_mapper[i]\n",
    "    print(movie_titles[movie_id])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010f4a8a",
   "metadata": {},
   "source": [
    "Do not worry if predicted top movies are not same as users top recommendations. Here, we completed the utility matrix and predicted which movies got highest rating. Based on particular user if the user has rated other movies which he did not rate previously, we now understand the user and estimated ratings he will give to new ones. May be some new movies which the user did not rate has got good ratings, hence we recommend those  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55d2fafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19</td>\n",
       "      <td>141</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Birdcage, The (1996)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910602</th>\n",
       "      <td>19</td>\n",
       "      <td>608</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060202</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3484351</th>\n",
       "      <td>19</td>\n",
       "      <td>656</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Eddie (1996)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3492718</th>\n",
       "      <td>19</td>\n",
       "      <td>710</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Celtic Pride (1996)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3466604</th>\n",
       "      <td>19</td>\n",
       "      <td>86</td>\n",
       "      <td>5.0</td>\n",
       "      <td>White Squall (1996)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475340</th>\n",
       "      <td>19</td>\n",
       "      <td>112</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Rumble in the Bronx (Hont faan kui) (1995)</td>\n",
       "      <td>Action|Adventure|Comedy|Crime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461423</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380253</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>19</td>\n",
       "      <td>62</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Mr. Holland's Opus (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         userId  movieId  rating                                       title  \\\n",
       "1732240      19      141     5.0                        Birdcage, The (1996)   \n",
       "1910602      19      608     5.0                                Fargo (1996)   \n",
       "2060202      19        7     5.0                              Sabrina (1995)   \n",
       "3484351      19      656     5.0                                Eddie (1996)   \n",
       "3492718      19      710     5.0                         Celtic Pride (1996)   \n",
       "3466604      19       86     5.0                         White Squall (1996)   \n",
       "3475340      19      112     5.0  Rumble in the Bronx (Hont faan kui) (1995)   \n",
       "1461423      19        6     5.0                                 Heat (1995)   \n",
       "380253       19        1     5.0                            Toy Story (1995)   \n",
       "5716         19       62     5.0                   Mr. Holland's Opus (1995)   \n",
       "\n",
       "                                              genres  \n",
       "1732240                                       Comedy  \n",
       "1910602                  Comedy|Crime|Drama|Thriller  \n",
       "2060202                               Comedy|Romance  \n",
       "3484351                                       Comedy  \n",
       "3492718                                       Comedy  \n",
       "3466604                       Action|Adventure|Drama  \n",
       "3475340                Action|Adventure|Comedy|Crime  \n",
       "1461423                        Action|Crime|Thriller  \n",
       "380253   Adventure|Animation|Children|Comedy|Fantasy  \n",
       "5716                                           Drama  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec = train.sort_values(by = ['rating'],ascending = False )\n",
    "rec = rec.loc[rec['userId'] == 19]\n",
    "rec.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a82f3db",
   "metadata": {},
   "source": [
    "References : https://www.jillcates.com/pydata-workshop/html/recommender_basics.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "789212b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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