Rec_System.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Embedding, Concatenate, Flatten, Dense, Dot, Add, Multiply, Subtract, Average, Reshape\n",
    "from keras.models import Model, Sequential\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_pickle('/home/elena/Projects/DLRM-with-Keras-for-movielens-dataset/ratings_movie_info.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             user_id       movie_id         rating\ncount  100836.000000  100836.000000  100836.000000\nmean      326.127564   19435.295718       3.501557\nstd       182.618491   35530.987199       1.042529\nmin         1.000000       1.000000       0.500000\n25%       177.000000    1199.000000       3.000000\n50%       325.000000    2991.000000       3.500000\n75%       477.000000    8122.000000       4.000000\nmax       610.000000  193609.000000       5.000000",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>100836.000000</td>\n      <td>100836.000000</td>\n      <td>100836.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>326.127564</td>\n      <td>19435.295718</td>\n      <td>3.501557</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>182.618491</td>\n      <td>35530.987199</td>\n      <td>1.042529</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>177.000000</td>\n      <td>1199.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>325.000000</td>\n      <td>2991.000000</td>\n      <td>3.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>477.000000</td>\n      <td>8122.000000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>610.000000</td>\n      <td>193609.000000</td>\n      <td>5.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "user_id             int64\nmovie_id            int64\nrating            float64\nunix_timestamp     object\ntitle              object\ngenre              object\ndtype: object"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "# Shuffle (reproducibly)\n",
    "df = df.sample(frac=1, random_state=1).reset_index(drop=True)\n",
    "\n",
    "# Partitioning train/val according to behaviour of keras.Model.fit() when called with\n",
    "# validation_split kwarg (which is to take validation data from the end as a contiguous\n",
    "# chunk)\n",
    "val_split = .15\n",
    "n_ratings = len(df)\n",
    "n_train = math.floor(n_ratings * (1-val_split))\n",
    "itrain = df.index[:n_train]\n",
    "ival = df.index[n_train:]\n",
    "train=df.loc[itrain]\n",
    "test=df.loc[ival]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "((85710, 7), (15126, 7), 610, 609)"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "train.shape, test.shape, train.user_id.unique().shape[0], test.user_id.unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(609, 0)"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "len(set(test.user_id.unique()).intersection(set(train.user_id.unique()))), len(set(test.user_id.unique()).difference(set(train.user_id.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# center rating\n",
    "mean_rating_train =  df.loc[itrain, 'rating'].mean()\n",
    "df['y'] = df['rating'] -mean_rating_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   user_id  movie_id  rating unix_timestamp                          title  \\\n0      275      1392     5.0     1049076638            Citizen Ruth (1996)   \n1      182      2841     4.0     1063284555          Stir of Echoes (1999)   \n2       89    167380     4.5     1520409150         A Dog's Purpose (2017)   \n3      369      1625     4.5     1237082077               Game, The (1997)   \n4      517       277     5.0     1487955694  Miracle on 34th Street (1994)   \n\n                     genre         y  \n0             Comedy|Drama  1.496897  \n1  Horror|Mystery|Thriller  0.496897  \n2             Comedy|Drama  0.996897  \n3   Drama|Mystery|Thriller  0.996897  \n4                    Drama  1.496897  ",
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     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['y'] = train['rating']-mean_rating_train\n",
    "test['y'] = test['rating'] - mean_rating_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_pickle('/home/elena/Projects/DLRM-with-Keras-for-movielens-dataset/ratings_movie_info_train.pkl')\n",
    "test.to_pickle('/home/elena/Projects/DLRM-with-Keras-for-movielens-dataset/ratings_movie_info_test.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "movie_embedding_size = 8\n",
    "user_embedding_size = 8\n",
    "\n",
    "def embedding_model(df, user_embedding_size, movie_embedding_size):\n",
    "    hidden_units = (32,4)\n",
    "\n",
    "    # Each instance will consist of two inputs: a single user id, and a single movie id\n",
    "    user_id_input = Input(shape=(1,), name='user_id')\n",
    "    movie_id_input = Input(shape=(1,), name='movie_id')\n",
    "    user_embedded = Embedding(df.user_id.max()+1, user_embedding_size, \n",
    "                                       input_length=1, name='user_embedding')(user_id_input)\n",
    "    movie_embedded = Embedding(df.movie_id.max()+1, movie_embedding_size, \n",
    "                                        input_length=1, name='movie_embedding')(movie_id_input)\n",
    "    # Concatenate the embeddings (and remove the useless extra dimension)\n",
    "    concatenated = Concatenate()([user_embedded, movie_embedded])\n",
    "    out = Flatten()(concatenated)\n",
    "\n",
    "    # Add one or more hidden layers\n",
    "    for n_hidden in hidden_units:\n",
    "        out = Dense(n_hidden, activation='relu')(out)\n",
    "\n",
    "    # A single output: our predicted rating\n",
    "    out = Dense(1, activation='linear', name='prediction')(out)\n",
    "\n",
    "    model = Model(inputs = [user_id_input, movie_id_input],outputs = out)\n",
    "    #compile\n",
    "    model.compile(optimizer = 'Adam',loss='MSE',metrics=['MAE'])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = embedding_model(df, 8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.6435 - MAE: 0.6101 - val_loss: 0.7100 - val_MAE: 0.6422\nEpoch 2/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.6232 - MAE: 0.5996 - val_loss: 0.7020 - val_MAE: 0.6359\nEpoch 3/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.6066 - MAE: 0.5903 - val_loss: 0.6962 - val_MAE: 0.6349\nEpoch 4/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.5917 - MAE: 0.5817 - val_loss: 0.6943 - val_MAE: 0.6310\nEpoch 5/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.5788 - MAE: 0.5743 - val_loss: 0.6944 - val_MAE: 0.6304\nEpoch 6/20\n172/172 [==============================] - 3s 16ms/step - loss: 0.5672 - MAE: 0.5676 - val_loss: 0.6949 - val_MAE: 0.6319\n"
    }
   ],
   "source": [
    "es=EarlyStopping(monitor='val_MAE', min_delta=0, patience=0, verbose=0, mode='min', baseline=None, restore_best_weights=False)\n",
    "history=model.fit(x=[df.user_id, df.movie_id], y=df.y, batch_size=500,epochs=20, verbose=1, validation_split = 0.15 , callbacks=[es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/20\n172/172 [==============================] - 24s 137ms/step - loss: 0.9133 - MAE: 0.7405 - val_loss: 0.7789 - val_MAE: 0.6726\nEpoch 2/20\n172/172 [==============================] - 23s 133ms/step - loss: 0.7110 - MAE: 0.6445 - val_loss: 0.7397 - val_MAE: 0.6558\nEpoch 3/20\n172/172 [==============================] - 23s 137ms/step - loss: 0.6508 - MAE: 0.6140 - val_loss: 0.7336 - val_MAE: 0.6530\nEpoch 4/20\n172/172 [==============================] - 24s 140ms/step - loss: 0.6182 - MAE: 0.5956 - val_loss: 0.7339 - val_MAE: 0.6545\n"
    }
   ],
   "source": [
    "model2 = embedding_model(df, 50, 50)\n",
    "history2=model2.fit(x=[df.user_id, df.movie_id], y=df.y, batch_size=500,epochs=20, verbose=1, validation_split = 0.15 , callbacks=[es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/20\n172/172 [==============================] - 23s 134ms/step - loss: 0.5907 - MAE: 0.5803 - val_loss: 0.7436 - val_MAE: 0.6600\nEpoch 2/20\n172/172 [==============================] - 24s 137ms/step - loss: 0.5637 - MAE: 0.5646 - val_loss: 0.7542 - val_MAE: 0.6600\nEpoch 3/20\n172/172 [==============================] - 24s 140ms/step - loss: 0.5338 - MAE: 0.5471 - val_loss: 0.7680 - val_MAE: 0.6671\n"
    }
   ],
   "source": [
    "model3 = embedding_model(df, 3, 3)\n",
    "history3=model2.fit(x=[df.user_id, df.movie_id], y=df.y, batch_size=500,epochs=20, verbose=1, validation_split = 0.15 , callbacks=[es])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Average rating in training set is 3.50 stars\nAlways predicting global average rating results in Mean Absolute Error=0.83, Mean Squared Error=1.09\nPredicting mean per movie results in Mean Absolute Error=0.75, Mean Squared Error=0.95\n"
    }
   ],
   "source": [
    "def get_metrics(y_true, y_pred):\n",
    "    return metrics.mean_absolute_error(y_true, y_pred), metrics.mean_squared_error(y_true, y_pred)\n",
    "\n",
    "mean_rating = train['rating'].mean()\n",
    "print(\"Average rating in training set is {:.2f} stars\".format(mean_rating))\n",
    "\n",
    "y_true = test['rating'].values\n",
    "always_mean = np.full(y_true.shape, mean_rating)\n",
    "\n",
    "mae, mse = get_metrics(y_true, always_mean)\n",
    "print(\"Always predicting global average rating results in Mean Absolute Error={:.2f}, Mean Squared Error={:.2f}\".format(\n",
    "    mae, mse))\n",
    "\n",
    "movies = df.copy().set_index('movie_id')\n",
    "mean_per_movie = train.groupby('movie_id')['rating'].mean()\n",
    "movies['mean_rating'] = mean_per_movie\n",
    "ratings_per_movie = train.groupby('movie_id').size()\n",
    "movies['n_ratings'] = ratings_per_movie\n",
    "# There are a few movies in the validation set not present in the training set. We'll just use the global\n",
    "# mean rating in their case.\n",
    "y_movie_mean = test.join(mean_per_movie, on='movie_id', rsuffix='mean')['ratingmean'].fillna(mean_rating).values\n",
    "\n",
    "mae, mse = get_metrics(y_true, y_movie_mean)\n",
    "print(\"Predicting mean per movie results in Mean Absolute Error={:.2f}, Mean Squared Error={:.2f}\".format(mae, mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "ax.plot(history.epoch, history.history['val_MAE'], label='Test MAE')\n",
    "ax.plot(history.epoch, history.history['MAE'], label='Training MAE')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.set_ylabel('Mean Absolute Error')\n",
    "ax.set_xlim(left=0, right=history.epoch[-1])\n",
    "baseline_mae = 0.75\n",
    "ax.axhline(baseline_mae, ls='--', label='Baseline', color='#002255', alpha=.5)\n",
    "ax.grid()\n",
    "fig.legend()\n",
    "plt.savefig('output.png', dpi=400, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "User #557 has rated 23 movies from the train dataset (avg. rating = 3.8):\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       movie_id  rating                                              title  \\\n78293      5618     5.0  Spirited Away (Sen to Chihiro no kamikakushi) ...   \n41027       648     5.0                         Mission: Impossible (1996)   \n40411      1036     5.0                                    Die Hard (1988)   \n5035         10     4.5                                   GoldenEye (1995)   \n7201       2571     4.5                                 Matrix, The (1999)   \n77460      1485     4.5                                   Liar Liar (1997)   \n15640      2617     4.5                                  Mummy, The (1999)   \n24033      4896     4.5  Harry Potter and the Sorcerer's Stone (a.k.a. ...   \n52338       150     4.5                                   Apollo 13 (1995)   \n66997     60069     4.0                                      WALL·E (2008)   \n2898       1291     4.0          Indiana Jones and the Last Crusade (1989)   \n24463       527     4.0                            Schindler's List (1993)   \n9491        344     4.0                  Ace Ventura: Pet Detective (1994)   \n31197      1721     3.5                                     Titanic (1997)   \n39845      1198     3.5  Raiders of the Lost Ark (Indiana Jones and the...   \n60972      2028     3.5                         Saving Private Ryan (1998)   \n10122       231     3.5             Dumb & Dumber (Dumb and Dumber) (1994)   \n28954      1580     3.0                   Men in Black (a.k.a. MIB) (1997)   \n27861       260     3.0          Star Wars: Episode IV - A New Hope (1977)   \n73927      1196     3.0  Star Wars: Episode V - The Empire Strikes Back...   \n34163      1210     3.0  Star Wars: Episode VI - Return of the Jedi (1983)   \n55697      2628     2.0   Star Wars: Episode I - The Phantom Menace (1999)   \n62905      2700     0.5        South Park: Bigger, Longer and Uncut (1999)   \n\n                                                 genre  \n78293                      Adventure|Animation|Fantasy  \n41027                Action|Adventure|Mystery|Thriller  \n40411                            Action|Crime|Thriller  \n5035                         Action|Adventure|Thriller  \n7201                            Action|Sci-Fi|Thriller  \n77460                                           Comedy  \n15640  Action|Adventure|Comedy|Fantasy|Horror|Thriller  \n24033                       Adventure|Children|Fantasy  \n52338                             Adventure|Drama|IMAX  \n66997      Adventure|Animation|Children|Romance|Sci-Fi  \n2898                                  Action|Adventure  \n24463                                        Drama|War  \n9491                                            Comedy  \n31197                                    Drama|Romance  \n39845                                 Action|Adventure  \n60972                                 Action|Drama|War  \n10122                                 Adventure|Comedy  \n28954                             Action|Comedy|Sci-Fi  \n27861                          Action|Adventure|Sci-Fi  \n73927                          Action|Adventure|Sci-Fi  \n34163                          Action|Adventure|Sci-Fi  \n55697                          Action|Adventure|Sci-Fi  \n62905                         Animation|Comedy|Musical  ",
      "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>rating</th>\n      <th>title</th>\n      <th>genre</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>78293</th>\n      <td>5618</td>\n      <td>5.0</td>\n      <td>Spirited Away (Sen to Chihiro no kamikakushi) ...</td>\n      <td>Adventure|Animation|Fantasy</td>\n    </tr>\n    <tr>\n      <th>41027</th>\n      <td>648</td>\n      <td>5.0</td>\n      <td>Mission: Impossible (1996)</td>\n      <td>Action|Adventure|Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>40411</th>\n      <td>1036</td>\n      <td>5.0</td>\n      <td>Die Hard (1988)</td>\n      <td>Action|Crime|Thriller</td>\n    </tr>\n    <tr>\n      <th>5035</th>\n      <td>10</td>\n      <td>4.5</td>\n      <td>GoldenEye (1995)</td>\n      <td>Action|Adventure|Thriller</td>\n    </tr>\n    <tr>\n      <th>7201</th>\n      <td>2571</td>\n      <td>4.5</td>\n      <td>Matrix, The (1999)</td>\n      <td>Action|Sci-Fi|Thriller</td>\n    </tr>\n    <tr>\n      <th>77460</th>\n      <td>1485</td>\n      <td>4.5</td>\n      <td>Liar Liar (1997)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>15640</th>\n      <td>2617</td>\n      <td>4.5</td>\n      <td>Mummy, The (1999)</td>\n      <td>Action|Adventure|Comedy|Fantasy|Horror|Thriller</td>\n    </tr>\n    <tr>\n      <th>24033</th>\n      <td>4896</td>\n      <td>4.5</td>\n      <td>Harry Potter and the Sorcerer's Stone (a.k.a. ...</td>\n      <td>Adventure|Children|Fantasy</td>\n    </tr>\n    <tr>\n      <th>52338</th>\n      <td>150</td>\n      <td>4.5</td>\n      <td>Apollo 13 (1995)</td>\n      <td>Adventure|Drama|IMAX</td>\n    </tr>\n    <tr>\n      <th>66997</th>\n      <td>60069</td>\n      <td>4.0</td>\n      <td>WALL·E (2008)</td>\n      <td>Adventure|Animation|Children|Romance|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>2898</th>\n      <td>1291</td>\n      <td>4.0</td>\n      <td>Indiana Jones and the Last Crusade (1989)</td>\n      <td>Action|Adventure</td>\n    </tr>\n    <tr>\n      <th>24463</th>\n      <td>527</td>\n      <td>4.0</td>\n      <td>Schindler's List (1993)</td>\n      <td>Drama|War</td>\n    </tr>\n    <tr>\n      <th>9491</th>\n      <td>344</td>\n      <td>4.0</td>\n      <td>Ace Ventura: Pet Detective (1994)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>31197</th>\n      <td>1721</td>\n      <td>3.5</td>\n      <td>Titanic (1997)</td>\n      <td>Drama|Romance</td>\n    </tr>\n    <tr>\n      <th>39845</th>\n      <td>1198</td>\n      <td>3.5</td>\n      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n      <td>Action|Adventure</td>\n    </tr>\n    <tr>\n      <th>60972</th>\n      <td>2028</td>\n      <td>3.5</td>\n      <td>Saving Private Ryan (1998)</td>\n      <td>Action|Drama|War</td>\n    </tr>\n    <tr>\n      <th>10122</th>\n      <td>231</td>\n      <td>3.5</td>\n      <td>Dumb &amp; Dumber (Dumb and Dumber) (1994)</td>\n      <td>Adventure|Comedy</td>\n    </tr>\n    <tr>\n      <th>28954</th>\n      <td>1580</td>\n      <td>3.0</td>\n      <td>Men in Black (a.k.a. MIB) (1997)</td>\n      <td>Action|Comedy|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>27861</th>\n      <td>260</td>\n      <td>3.0</td>\n      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>73927</th>\n      <td>1196</td>\n      <td>3.0</td>\n      <td>Star Wars: Episode V - The Empire Strikes Back...</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>34163</th>\n      <td>1210</td>\n      <td>3.0</td>\n      <td>Star Wars: Episode VI - Return of the Jedi (1983)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>55697</th>\n      <td>2628</td>\n      <td>2.0</td>\n      <td>Star Wars: Episode I - The Phantom Menace (1999)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>62905</th>\n      <td>2700</td>\n      <td>0.5</td>\n      <td>South Park: Bigger, Longer and Uncut (1999)</td>\n      <td>Animation|Comedy|Musical</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 152
    }
   ],
   "source": [
    "#Example predictions\n",
    "ratings_per_user = df.groupby('user_id').size()\n",
    "uid = ratings_per_user[ratings_per_user < 30].sample(1, random_state=1).index[0]\n",
    "user_ratings_train = train[train.user_id==uid]\n",
    "print(\"User #{} has rated {} movies from the train dataset (avg. rating = {:.1f}):\".format(\n",
    "    uid, len(user_ratings_train), user_ratings_train['rating'].mean(),\n",
    "))\n",
    "cols = ['user_id', 'movie_id', 'rating', 'title', 'genre']\n",
    "user_ratings_train.sort_values(by='rating', ascending=False)[['movie_id', 'rating','title','genre']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_ratings_test = test[test.user_id == uid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       user_id  movie_id  rating unix_timestamp  \\\n89235      557       165     5.0     1452797740   \n94849      557    134130     3.5     1452934442   \n95106      557       318     4.5     1452797268   \n99995      557      4306     4.5     1452797727   \n\n                                   title  \\\n89235  Die Hard: With a Vengeance (1995)   \n94849                 The Martian (2015)   \n95106   Shawshank Redemption, The (1994)   \n99995                       Shrek (2001)   \n\n                                                   genre         y  \n89235                              Action|Crime|Thriller  1.496897  \n94849                             Adventure|Drama|Sci-Fi -0.003103  \n95106                                        Crime|Drama  0.996897  \n99995  Adventure|Animation|Children|Comedy|Fantasy|Ro...  0.996897  ",
      "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>unix_timestamp</th>\n      <th>title</th>\n      <th>genre</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>89235</th>\n      <td>557</td>\n      <td>165</td>\n      <td>5.0</td>\n      <td>1452797740</td>\n      <td>Die Hard: With a Vengeance (1995)</td>\n      <td>Action|Crime|Thriller</td>\n      <td>1.496897</td>\n    </tr>\n    <tr>\n      <th>94849</th>\n      <td>557</td>\n      <td>134130</td>\n      <td>3.5</td>\n      <td>1452934442</td>\n      <td>The Martian (2015)</td>\n      <td>Adventure|Drama|Sci-Fi</td>\n      <td>-0.003103</td>\n    </tr>\n    <tr>\n      <th>95106</th>\n      <td>557</td>\n      <td>318</td>\n      <td>4.5</td>\n      <td>1452797268</td>\n      <td>Shawshank Redemption, The (1994)</td>\n      <td>Crime|Drama</td>\n      <td>0.996897</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td>557</td>\n      <td>4306</td>\n      <td>4.5</td>\n      <td>1452797727</td>\n      <td>Shrek (2001)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy|Ro...</td>\n      <td>0.996897</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 80
    }
   ],
   "source": [
    "user_ratings_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting for user uid movies from test set\n",
    "y_pred = model.predict([user_ratings_test.user_id, user_ratings_test.movie_id])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = user_ratings_test.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_delta = mean_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(4, 1)"
     },
     "metadata": {},
     "execution_count": 84
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame()\n",
    "result['predicted_rating'] = np.squeeze(y_pred) + y_delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['true_rating'] = y_true.values + y_delta\n",
    "result['movie_id'] = user_ratings_test.movie_id.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   movie_id  true_rating  predicted_rating     error\n1    134130          3.5          3.905294  0.405294\n2       318          4.5          4.035668  0.464332\n3      4306          4.5          4.001062  0.498938\n0       165          5.0          3.707034  1.292966",
      "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>true_rating</th>\n      <th>predicted_rating</th>\n      <th>error</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>134130</td>\n      <td>3.5</td>\n      <td>3.905294</td>\n      <td>0.405294</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>318</td>\n      <td>4.5</td>\n      <td>4.035668</td>\n      <td>0.464332</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4306</td>\n      <td>4.5</td>\n      <td>4.001062</td>\n      <td>0.498938</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>165</td>\n      <td>5.0</td>\n      <td>3.707034</td>\n      <td>1.292966</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 87
    }
   ],
   "source": [
    "result['error'] = abs(np.squeeze(y_pred) - np.squeeze(y_true.values))\n",
    "result.sort_values(by='error', ascending=True)[['movie_id','true_rating', 'predicted_rating', 'error']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.6653824218497222"
     },
     "metadata": {},
     "execution_count": 88
    }
   ],
   "source": [
    "result.error.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   true_rating  predicted_rating     error                              title  \\\n1          3.5          3.905294  0.405294                 The Martian (2015)   \n2          4.5          4.035668  0.464332   Shawshank Redemption, The (1994)   \n3          4.5          4.001062  0.498938                       Shrek (2001)   \n0          5.0          3.707034  1.292966  Die Hard: With a Vengeance (1995)   \n\n                                               genre  \n1                             Adventure|Drama|Sci-Fi  \n2                                        Crime|Drama  \n3  Adventure|Animation|Children|Comedy|Fantasy|Ro...  \n0                              Action|Crime|Thriller  ",
      "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>true_rating</th>\n      <th>predicted_rating</th>\n      <th>error</th>\n      <th>title</th>\n      <th>genre</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>3.5</td>\n      <td>3.905294</td>\n      <td>0.405294</td>\n      <td>The Martian (2015)</td>\n      <td>Adventure|Drama|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.5</td>\n      <td>4.035668</td>\n      <td>0.464332</td>\n      <td>Shawshank Redemption, The (1994)</td>\n      <td>Crime|Drama</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.5</td>\n      <td>4.001062</td>\n      <td>0.498938</td>\n      <td>Shrek (2001)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy|Ro...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>5.0</td>\n      <td>3.707034</td>\n      <td>1.292966</td>\n      <td>Die Hard: With a Vengeance (1995)</td>\n      <td>Action|Crime|Thriller</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 151
    }
   ],
   "source": [
    "pd.merge(user_ratings_test, result, on='movie_id').sort_values(by='error', ascending=True)[['true_rating', 'predicted_rating', 'error','title', 'genre']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# die hard investigation\n",
    "diehard=df[(df.movie_id==165) | (df.movie_id==1036)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "diehard_train=train[(train.movie_id==165) | (train.movie_id==1036)]\n",
    "diehard_test=test[(test.movie_id==165) | (test.movie_id==1036)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "movie_id\n165     3.555556\n1036    3.862069\nName: rating, dtype: float64"
     },
     "metadata": {},
     "execution_count": 159
    }
   ],
   "source": [
    "diehard.groupby('movie_id').mean()['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "movie_id\n165     3.500000\n1036    3.886179\nName: rating, dtype: float64"
     },
     "metadata": {},
     "execution_count": 160
    }
   ],
   "source": [
    "diehard_train.groupby('movie_id').mean()['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "movie_id\n165     3.785714\n1036    3.727273\nName: rating, dtype: float64"
     },
     "metadata": {},
     "execution_count": 161
    }
   ],
   "source": [
    "diehard_test.groupby('movie_id').mean()['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show top 10 movie recommendations to a user\n",
    "# Let us get a user and see the top recommendations.\n",
    "movies_watched_by_user = df[df.user_id == uid]\n",
    "movies_not_watched = list(set(df.movie_id.unique()).difference(set(movies_watched_by_user.movie_id.unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "list"
     },
     "metadata": {},
     "execution_count": 116
    }
   ],
   "source": [
    "type(movies_not_watched)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_not_watched=pd.DataFrame(movies_not_watched, columns = ['movie_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   movie_id  user_id\n0         1      557\n1         2      557\n2         3      557\n3         4      557\n4         5      557",
      "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>user_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>557</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>557</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>557</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>557</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>557</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 134
    }
   ],
   "source": [
    "movies_not_watched['user_id'] = uid\n",
    "movies_not_watched.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_user = model.predict([movies_not_watched.user_id, movies_not_watched.movie_id])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_not_watched['predicted_ratings'] = ratings_user +y_delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_not_watched=movies_not_watched.sort_values(by='predicted_ratings', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      movie_id  user_id  predicted_ratings\n7031    177593      557           4.660729\n9488     64197      557           4.556700\n7254    113829      557           4.553651\n5574      7121      557           4.538522\n3480      4142      557           4.538311\n6658    142020      557           4.530828\n3984      4788      557           4.526130\n6595    164917      557           4.520685\n6515    107951      557           4.515440\n7662    117192      557           4.501959",
      "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>user_id</th>\n      <th>predicted_ratings</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7031</th>\n      <td>177593</td>\n      <td>557</td>\n      <td>4.660729</td>\n    </tr>\n    <tr>\n      <th>9488</th>\n      <td>64197</td>\n      <td>557</td>\n      <td>4.556700</td>\n    </tr>\n    <tr>\n      <th>7254</th>\n      <td>113829</td>\n      <td>557</td>\n      <td>4.553651</td>\n    </tr>\n    <tr>\n      <th>5574</th>\n      <td>7121</td>\n      <td>557</td>\n      <td>4.538522</td>\n    </tr>\n    <tr>\n      <th>3480</th>\n      <td>4142</td>\n      <td>557</td>\n      <td>4.538311</td>\n    </tr>\n    <tr>\n      <th>6658</th>\n      <td>142020</td>\n      <td>557</td>\n      <td>4.530828</td>\n    </tr>\n    <tr>\n      <th>3984</th>\n      <td>4788</td>\n      <td>557</td>\n      <td>4.526130</td>\n    </tr>\n    <tr>\n      <th>6595</th>\n      <td>164917</td>\n      <td>557</td>\n      <td>4.520685</td>\n    </tr>\n    <tr>\n      <th>6515</th>\n      <td>107951</td>\n      <td>557</td>\n      <td>4.515440</td>\n    </tr>\n    <tr>\n      <th>7662</th>\n      <td>117192</td>\n      <td>557</td>\n      <td>4.501959</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 138
    }
   ],
   "source": [
    "movies_not_watched.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "       user_id  movie_id  rating unix_timestamp  \\\n4866       598    113829     5.0     1430616765   \n11864      419     64197     5.0     1321853498   \n12489      380    117192     4.0     1494709303   \n15944      184    164917     4.5     1537108221   \n16206      212    177593     5.0     1527794935   \n\n                                                  title                 genre  \\\n4866                             One I Love, The (2014)  Comedy|Drama|Romance   \n11864                                     Hunger (2008)                 Drama   \n12489         Doctor Who: The Time of the Doctor (2013)       Adventure|Drama   \n15944                                       13th (2016)           Documentary   \n16206  Three Billboards Outside Ebbing, Missouri (2017)           Crime|Drama   \n\n              y  \n4866   1.496897  \n11864  1.496897  \n12489  0.496897  \n15944  0.996897  \n16206  1.496897  ",
      "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>unix_timestamp</th>\n      <th>title</th>\n      <th>genre</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4866</th>\n      <td>598</td>\n      <td>113829</td>\n      <td>5.0</td>\n      <td>1430616765</td>\n      <td>One I Love, The (2014)</td>\n      <td>Comedy|Drama|Romance</td>\n      <td>1.496897</td>\n    </tr>\n    <tr>\n      <th>11864</th>\n      <td>419</td>\n      <td>64197</td>\n      <td>5.0</td>\n      <td>1321853498</td>\n      <td>Hunger (2008)</td>\n      <td>Drama</td>\n      <td>1.496897</td>\n    </tr>\n    <tr>\n      <th>12489</th>\n      <td>380</td>\n      <td>117192</td>\n      <td>4.0</td>\n      <td>1494709303</td>\n      <td>Doctor Who: The Time of the Doctor (2013)</td>\n      <td>Adventure|Drama</td>\n      <td>0.496897</td>\n    </tr>\n    <tr>\n      <th>15944</th>\n      <td>184</td>\n      <td>164917</td>\n      <td>4.5</td>\n      <td>1537108221</td>\n      <td>13th (2016)</td>\n      <td>Documentary</td>\n      <td>0.996897</td>\n    </tr>\n    <tr>\n      <th>16206</th>\n      <td>212</td>\n      <td>177593</td>\n      <td>5.0</td>\n      <td>1527794935</td>\n      <td>Three Billboards Outside Ebbing, Missouri (2017)</td>\n      <td>Crime|Drama</td>\n      <td>1.496897</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 164
    }
   ],
   "source": [
    "df[df[\"movie_id\"].isin(top_movies_user)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Showing recommendations for user: 557\n====================================\n--------------------------------\nTop 10 movie recommendations\n--------------------------------\nOne I Love, The (2014) : Comedy|Drama|Romance\nHunger (2008) : Drama\nDoctor Who: The Time of the Doctor (2013) : Adventure|Drama\n13th (2016) : Documentary\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nDoctor Who: The Time of the Doctor (2013) : Adventure|Drama\nDoctor Who: The Time of the Doctor (2013) : Adventure|Drama\nHunger (2008) : Drama\n13th (2016) : Documentary\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nAdam's Rib (1949) : Comedy|Romance\nLeft Behind: The Movie (2000) : Action|Adventure|Drama|Thriller\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nHunting Elephants (2013) : Comedy|Crime\nAdam's Rib (1949) : Comedy|Romance\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nOscar (1967) : Comedy\nAdam's Rib (1949) : Comedy|Romance\nMoscow Does Not Believe in Tears (Moskva slezam ne verit) (1979) : Drama|Romance\nAdam's Rib (1949) : Comedy|Romance\nLeft Behind: The Movie (2000) : Action|Adventure|Drama|Thriller\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\nThree Billboards Outside Ebbing, Missouri (2017) : Crime|Drama\n"
    }
   ],
   "source": [
    "print(\"Showing recommendations for user: {}\".format(uid))\n",
    "print(\"====\" * 9)\n",
    "top_movies_user = movies_not_watched.head(10).movie_id.values\n",
    "\n",
    "print(\"----\" * 8)\n",
    "print(\"Top 10 movie recommendations\")\n",
    "print(\"----\" * 8)\n",
    "recommended_movies = df[df[\"movie_id\"].isin(top_movies_user)]\n",
    "recommended_movies = recommended_movies.drop_duplicates()\n",
    "for row in recommended_movies.itertuples():\n",
    "    print(row.title, \":\", row.genre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}