Model-based collaborative filtering.ipynb

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  {
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   "source": [
    "![title](Chapter-2.png \"Header\")\n",
    "___\n",
    "# Chapter 2 -  Machine Learning Based Recommendation Systems\n",
    "## Segment 2 - Model-based Collaborative Filtering Systems\n",
    "## SVD Matrix Factorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import sklearn\n",
    "from sklearn.decomposition import TruncatedSVD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MovieLens dataset was collected by the GroupLens Research Project at the University of Minnesota. You can download the dataset for this demostration at the following URL: https://grouplens.org/datasets/movielens/100k/\n",
    "\n",
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
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       "   user_id  item_id  rating  timestamp\n",
       "0      196      242       3  881250949\n",
       "1      186      302       3  891717742\n",
       "2       22      377       1  878887116\n",
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   "source": [
    "columns = ['user_id', 'item_id', 'rating', 'timestamp']\n",
    "frame = pd.read_csv('ml-100k/u.data', sep='\\t', names=columns)\n",
    "frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>movie title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
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      "text/plain": [
       "   item_id        movie title\n",
       "0        1   Toy Story (1995)\n",
       "1        2   GoldenEye (1995)\n",
       "2        3  Four Rooms (1995)\n",
       "3        4  Get Shorty (1995)\n",
       "4        5     Copycat (1995)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "columns = ['item_id', 'movie title', 'release date', 'video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure',\n",
    "          'Animation', 'Childrens', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror',\n",
    "          'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n",
    "\n",
    "movies = pd.read_csv('ml-100k/u.item', sep='|', names=columns, encoding='latin-1')\n",
    "movie_names = movies[['item_id', 'movie title']]\n",
    "movie_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th>rating</th>\n",
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       "      <th>movie title</th>\n",
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       "      <td>Kolya (1996)</td>\n",
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       "      <td>Kolya (1996)</td>\n",
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       "      <td>Kolya (1996)</td>\n",
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       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
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       "   user_id  item_id  rating  timestamp   movie title\n",
       "0      196      242       3  881250949  Kolya (1996)\n",
       "1       63      242       3  875747190  Kolya (1996)\n",
       "2      226      242       5  883888671  Kolya (1996)\n",
       "3      154      242       3  879138235  Kolya (1996)\n",
       "4      306      242       5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_movies_data = pd.merge(frame, movie_names, on='item_id')\n",
    "combined_movies_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_id\n",
       "50     583\n",
       "258    509\n",
       "100    508\n",
       "181    507\n",
       "294    485\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_movies_data.groupby('item_id')['rating'].count().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Star Wars (1977)'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter = combined_movies_data['item_id']==50\n",
    "combined_movies_data[filter]['movie title'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Utility Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <th>'Til There Was You (1997)</th>\n",
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       "      <th>Young Guns (1988)</th>\n",
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       "movie title  'Til There Was You (1997)  1-900 (1994)  101 Dalmatians (1996)  \\\n",
       "user_id                                                                       \n",
       "1                                    0             0                      2   \n",
       "2                                    0             0                      0   \n",
       "3                                    0             0                      0   \n",
       "4                                    0             0                      0   \n",
       "5                                    0             0                      2   \n",
       "\n",
       "movie title  12 Angry Men (1957)  187 (1997)  2 Days in the Valley (1996)  \\\n",
       "user_id                                                                     \n",
       "1                              5           0                            0   \n",
       "2                              0           0                            0   \n",
       "3                              0           2                            0   \n",
       "4                              0           0                            0   \n",
       "5                              0           0                            0   \n",
       "\n",
       "movie title  20,000 Leagues Under the Sea (1954)  \\\n",
       "user_id                                            \n",
       "1                                              3   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "5                                              0   \n",
       "\n",
       "movie title  2001: A Space Odyssey (1968)  \\\n",
       "user_id                                     \n",
       "1                                       4   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "5                                       4   \n",
       "\n",
       "movie title  3 Ninjas: High Noon At Mega Mountain (1998)  \\\n",
       "user_id                                                    \n",
       "1                                                      0   \n",
       "2                                                      1   \n",
       "3                                                      0   \n",
       "4                                                      0   \n",
       "5                                                      0   \n",
       "\n",
       "movie title  39 Steps, The (1935)  ...  Yankee Zulu (1994)  \\\n",
       "user_id                            ...                       \n",
       "1                               0  ...                   0   \n",
       "2                               0  ...                   0   \n",
       "3                               0  ...                   0   \n",
       "4                               0  ...                   0   \n",
       "5                               0  ...                   0   \n",
       "\n",
       "movie title  Year of the Horse (1997)  You So Crazy (1994)  \\\n",
       "user_id                                                      \n",
       "1                                   0                    0   \n",
       "2                                   0                    0   \n",
       "3                                   0                    0   \n",
       "4                                   0                    0   \n",
       "5                                   0                    0   \n",
       "\n",
       "movie title  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
       "user_id                                                     \n",
       "1                                    5                  3   \n",
       "2                                    0                  0   \n",
       "3                                    0                  0   \n",
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       "5                                    4                  0   \n",
       "\n",
       "movie title  Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
       "user_id                                                                    \n",
       "1                               0                                      0   \n",
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       "movie title  Zeus and Roxanne (1997)  unknown  \\\n",
       "user_id                                         \n",
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       "5                                  0        4   \n",
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       "movie title  Á köldum klaka (Cold Fever) (1994)  \n",
       "user_id                                          \n",
       "1                                             0  \n",
       "2                                             0  \n",
       "3                                             0  \n",
       "4                                             0  \n",
       "5                                             0  \n",
       "\n",
       "[5 rows x 1664 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_crosstab = combined_movies_data.pivot_table(values='rating', index='user_id', columns='movie title', fill_value=0)\n",
    "rating_crosstab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transposing the Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(943, 1664)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_crosstab.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1664, 943)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = rating_crosstab.values.T\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decomposing the Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1664, 12)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVD = TruncatedSVD(n_components=12, random_state=17)\n",
    "\n",
    "resultant_matrix = SVD.fit_transform(X)\n",
    "\n",
    "resultant_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating a Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1664, 1664)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = np.corrcoef(resultant_matrix)\n",
    "corr_mat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolating Star Wars From the Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1398"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_names = rating_crosstab.columns\n",
    "movies_list = list(movie_names)\n",
    "\n",
    "star_wars = movies_list.index('Star Wars (1977)')\n",
    "star_wars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1664,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_star_wars = corr_mat[1398]\n",
    "corr_star_wars.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommending a Highly Correlated Movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Die Hard (1988)',\n",
       " 'Empire Strikes Back, The (1980)',\n",
       " 'Fugitive, The (1993)',\n",
       " 'Raiders of the Lost Ark (1981)',\n",
       " 'Return of the Jedi (1983)',\n",
       " 'Star Wars (1977)',\n",
       " 'Terminator 2: Judgment Day (1991)',\n",
       " 'Terminator, The (1984)',\n",
       " 'Toy Story (1995)']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(movie_names[(corr_star_wars<1.0) & (corr_star_wars > 0.9)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Return of the Jedi (1983)', 'Star Wars (1977)']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(movie_names[(corr_star_wars<1.0) & (corr_star_wars > 0.95)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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