Content_Based_Genero.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read dataframes\n",
    "df_movies = pd.read_csv('ml-latest-small/movies.csv')\n",
    "df_ratings = pd.read_csv('ml-latest-small/ratings.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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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",
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       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
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       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 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",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \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",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies['genres']=df_movies['genres'].apply(lambda x: re.sub(r'\\|\\b',' ',x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "genero = df_movies.genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_movies_tf_idf_described = tf_idf.fit_transform(genero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2m = cosine_similarity(df_movies_tf_idf_described)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9742, 24)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies_tf_idf_described.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tfidf_m2m = pd.DataFrame(cosine_similarity(df_movies_tf_idf_described))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_movie_id = df_movies['movieId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tfidf_m2m.columns = [str(index_to_movie_id[int(col)]) for col in df_tfidf_m2m.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tfidf_m2m.index = [index_to_movie_id[idx] for idx in df_tfidf_m2m.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "193581  0.680258  0.341376  0.181883  0.160888  0.318581  0.239513  0.181883   \n",
       "193583  0.755891  0.379331  0.202105  0.178776  0.354002  0.000000  0.202105   \n",
       "193585  0.000000  0.000000  0.000000  0.466405  0.000000  0.000000  0.000000   \n",
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       "193609  0.267586  0.000000  0.570915  0.505015  1.000000  0.000000  0.570915   \n",
       "\n",
       "               8         9        10  ...    193565    193567    193571  \\\n",
       "1       0.654698  0.000000  0.262413  ...  0.360397  0.465621  0.196578   \n",
       "2       0.804715  0.000000  0.322542  ...  0.000000  0.000000  0.000000   \n",
       "3       0.000000  0.000000  0.000000  ...  0.162848  0.000000  0.419413   \n",
       "4       0.000000  0.000000  0.000000  ...  0.144051  0.201391  0.687440   \n",
       "5       0.000000  0.000000  0.000000  ...  0.285240  0.000000  0.734632   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "193581  0.000000  0.436010  0.241142  ...  0.599288  0.554355  0.234040   \n",
       "193583  0.000000  0.000000  0.000000  ...  0.476784  0.615990  0.260061   \n",
       "193585  0.000000  0.000000  0.000000  ...  0.000000  0.431794  0.678466   \n",
       "193587  0.000000  0.578606  0.320007  ...  0.674692  0.735655  0.000000   \n",
       "193609  0.000000  0.000000  0.000000  ...  0.285240  0.000000  0.734632   \n",
       "\n",
       "          193573  193579    193581    193583    193585    193587    193609  \n",
       "1       0.516225     0.0  0.680258  0.755891  0.000000  0.421037  0.267586  \n",
       "2       0.000000     0.0  0.341376  0.379331  0.000000  0.000000  0.000000  \n",
       "3       0.000000     0.0  0.181883  0.202105  0.000000  0.000000  0.570915  \n",
       "4       0.000000     0.0  0.160888  0.178776  0.466405  0.000000  0.505015  \n",
       "5       0.000000     0.0  0.318581  0.354002  0.000000  0.000000  1.000000  \n",
       "...          ...     ...       ...       ...       ...       ...       ...  \n",
       "193581  0.614603     0.0  1.000000  0.899942  0.000000  0.753553  0.318581  \n",
       "193583  0.682937     0.0  0.899942  1.000000  0.000000  0.557008  0.354002  \n",
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       "[9742 rows x 9742 columns]"
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_tfidf_m2m"
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 54,
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    "df_tfidf_m2m.iloc[0].sort_values(ascending=False)[1:11]"
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  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   movieId                    title          genres\n",
       "2        3  Grumpier Old Men (1995)  Comedy Romance"
      ]
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     "execution_count": 64,
     "metadata": {},
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   "source": [
    "df_movies[df_movies.movieId == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user_ratings = df_ratings[df_ratings.userId == 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user_data_with_tags = df_data_with_tags.reset_index().merge(df_user_ratings, on='movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "                                                 title  rating\n",
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       "2                Ace Ventura: When Nature Calls (1995)     1.0\n",
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       "4                                          Babe (1995)     3.5\n",
       "..                                                 ...     ...\n",
       "237  Star Wars: Episode VII - The Force Awakens (2015)     3.0\n",
       "238                                 The Martian (2015)     3.0\n",
       "239                                  Inside Out (2015)     4.0\n",
       "240                                   Spotlight (2015)     4.5\n",
       "241                              Big Short, The (2015)     4.5\n",
       "\n",
       "[242 rows x 2 columns]"
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     "execution_count": 70,
     "metadata": {},
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   "source": [
    "df_user_data_with_tags[['title', 'rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user_data_with_tags['weight'] = df_user_data_with_tags['rating']/5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     index  movieId                                              title  \\\n",
       "0        0        1                                   Toy Story (1995)   \n",
       "1        1        2                                     Jumanji (1995)   \n",
       "2       18       19              Ace Ventura: When Nature Calls (1995)   \n",
       "3       31       32          Twelve Monkeys (a.k.a. 12 Monkeys) (1995)   \n",
       "4       33       34                                        Babe (1995)   \n",
       "..     ...      ...                                                ...   \n",
       "237  12105   122886  Star Wars: Episode VII - The Force Awakens (2015)   \n",
       "238  12337   134130                                 The Martian (2015)   \n",
       "239  12357   134853                                  Inside Out (2015)   \n",
       "240  12526   142488                                   Spotlight (2015)   \n",
       "241  12625   148626                              Big Short, The (2015)   \n",
       "\n",
       "                                                genres  rating_mean  \\\n",
       "0          Adventure|Animation|Children|Comedy|Fantasy     3.886649   \n",
       "1                           Adventure|Children|Fantasy     3.246583   \n",
       "2                                               Comedy     2.642014   \n",
       "3                              Mystery|Sci-Fi|Thriller     3.890193   \n",
       "4                                       Children|Drama     3.613834   \n",
       "..                                                 ...          ...   \n",
       "237               Action|Adventure|Fantasy|Sci-Fi|IMAX     3.794736   \n",
       "238                             Adventure|Drama|Sci-Fi     4.043812   \n",
       "239  Adventure|Animation|Children|Comedy|Drama|Fantasy     3.960429   \n",
       "240                                           Thriller     4.079351   \n",
       "241                                              Drama     4.019956   \n",
       "\n",
       "     rating_median  num_ratingsdf_tags_per_movie  \\\n",
       "0              4.0                       68469.0   \n",
       "1              3.0                       27143.0   \n",
       "2              3.0                       24913.0   \n",
       "3              4.0                       54846.0   \n",
       "4              4.0                       35903.0   \n",
       "..             ...                           ...   \n",
       "237            4.0                       12747.0   \n",
       "238            4.0                       16160.0   \n",
       "239            4.0                       13659.0   \n",
       "240            4.0                        5211.0   \n",
       "241            4.0                        6765.0   \n",
       "\n",
       "                                            movie_tags  userId  rating  \\\n",
       "0    982 338 1093 664 830 990 1114 519 743 472 19 2...    2000     4.0   \n",
       "1    112 1093 396 1113 502 743 19 244 51 777 889 62...    2000     3.5   \n",
       "2    763 558 594 893 216 418 777 801 62 265 264 807...    2000     1.0   \n",
       "3    538 982 52 222 558 1093 243 396 547 46 715 830...    2000     4.0   \n",
       "4    338 46 1114 244 62 61 603 497 22 378 469 468 6...    2000     3.5   \n",
       "..                                                 ...     ...     ...   \n",
       "237  982 52 222 833 1005 338 850 1093 396 911 502 9...    2000     3.0   \n",
       "238  555 338 1093 243 396 46 830 945 1114 519 743 1...    2000     3.0   \n",
       "239  52 338 1093 664 46 871 1114 519 743 1024 216 4...    2000     4.0   \n",
       "240  982 833 262 555 558 338 850 243 715 567 682 11...    2000     4.5   \n",
       "241  982 52 558 555 338 243 567 1114 519 743 1024 4...    2000     4.5   \n",
       "\n",
       "      timestamp  weight  \n",
       "0    1455860873     0.8  \n",
       "1    1456119319     0.7  \n",
       "2    1455861164     0.2  \n",
       "3    1455998568     0.8  \n",
       "4    1455861041     0.7  \n",
       "..          ...     ...  \n",
       "237  1455823417     0.6  \n",
       "238  1455999133     0.6  \n",
       "239  1455998854     0.8  \n",
       "240  1455823423     0.9  \n",
       "241  1455823419     0.9  \n",
       "\n",
       "[242 rows x 12 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_data_with_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_profile = np.dot(df_movies_tf_idf_described[df_user_data_with_tags['index'].values].toarray().T, df_user_data_with_tags['weight'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = cosine_similarity(atleast_2d(user_profile), df_movies_tf_idf_described)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = argsort(C)[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "recommendations = [i for i in R[0] if i not in df_user_data_with_tags['index'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13157                                 Isle of Dogs (2018)\n",
       "12858                         Over the Garden Wall (2013)\n",
       "13081                            Blade Runner 2049 (2017)\n",
       "13106                                         Coco (2017)\n",
       "12395          George Carlin: You Are All Diseased (1999)\n",
       "1136                                   Stand by Me (1986)\n",
       "13100    Three Billboards Outside Ebbing, Missouri (2017)\n",
       "820                                 Apartment, The (1960)\n",
       "13040             The Godfather Trilogy: 1972-1990 (1992)\n",
       "12689                    Hunt for the Wilderpeople (2016)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_with_tags['title'][recommendations].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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