ml-25m recommend.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import data and Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies  = pd.read_csv('ml-25m\\movies.csv')\n",
    "tags = pd.read_csv('ml-25m/tags.csv')\n",
    "ratings = pd.read_csv('ml-25m/ratings.csv')\n",
    "genome_score = pd.read_csv('ml-25m/genome-scores.csv')\n",
    "genome_tags = pd.read_csv('ml-25m/genome-tags.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                    title  \\\n",
       "0        1         Toy Story (1995)   \n",
       "1        2           Jumanji (1995)   \n",
       "2        3  Grumpier Old Men (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62423, 3)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>260</td>\n",
       "      <td>classic</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>260</td>\n",
       "      <td>sci-fi</td>\n",
       "      <td>1439472256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1732</td>\n",
       "      <td>dark comedy</td>\n",
       "      <td>1573943598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   userId  movieId          tag   timestamp\n",
       "0       3      260      classic  1439472355\n",
       "1       3      260       sci-fi  1439472256\n",
       "2       4     1732  dark comedy  1573943598"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <td>5.0</td>\n",
       "      <td>1147880044</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3.5</td>\n",
       "      <td>1147868817</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>5.0</td>\n",
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      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      296     5.0  1147880044\n",
       "1       1      306     3.5  1147868817\n",
       "2       1      307     5.0  1147868828"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000095, 4)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tagId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>007 (series)</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18th century</td>\n",
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      ],
      "text/plain": [
       "   tagId           tag\n",
       "0      1           007\n",
       "1      2  007 (series)\n",
       "2      3  18th century"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_tags.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "genome_tags = genome_tags[~genome_tags['tag'].isin(['original', 'sequel', 'good sequel','sequels'])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tagId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>007</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>007 (series)</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18th century</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1920s</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1930s</td>\n",
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       "      <th>...</th>\n",
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       "      <th>1123</th>\n",
       "      <td>1124</td>\n",
       "      <td>writing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>1125</td>\n",
       "      <td>wuxia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>1126</td>\n",
       "      <td>wwii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1126</th>\n",
       "      <td>1127</td>\n",
       "      <td>zombie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1128</td>\n",
       "      <td>zombies</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>1124 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tagId           tag\n",
       "0         1           007\n",
       "1         2  007 (series)\n",
       "2         3  18th century\n",
       "3         4         1920s\n",
       "4         5         1930s\n",
       "...     ...           ...\n",
       "1123   1124       writing\n",
       "1124   1125         wuxia\n",
       "1125   1126          wwii\n",
       "1126   1127        zombie\n",
       "1127   1128       zombies\n",
       "\n",
       "[1124 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
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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>tagId</th>\n",
       "      <th>relevance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02875</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.02375</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.06250</td>\n",
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      ],
      "text/plain": [
       "   movieId  tagId  relevance\n",
       "0        1      1    0.02875\n",
       "1        1      2    0.02375\n",
       "2        1      3    0.06250"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genome_score.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.merge(genome_score, genome_tags, on='tagId')\n",
    "\n",
    "\n",
    "top_tags = merged.groupby('movieId').apply(lambda x: x.nlargest(5, 'relevance')['tag'].tolist())\n",
    "top_tags_df = top_tags.reset_index(name='top_relevance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[toys, computer animation, pixar animation, ki...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[adventure, children, fantasy, kids, special e...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[comedy, gunfight, romance, destiny, great]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[women, chick flick, divorce, girlie movie, ro...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[father daughter relationship, pregnancy, midl...</td>\n",
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      "text/plain": [
       "   movieId                                      top_relevance\n",
       "0        1  [toys, computer animation, pixar animation, ki...\n",
       "1        2  [adventure, children, fantasy, kids, special e...\n",
       "2        3        [comedy, gunfight, romance, destiny, great]\n",
       "3        4  [women, chick flick, divorce, girlie movie, ro...\n",
       "4        5  [father daughter relationship, pregnancy, midl..."
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_tags_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.merge(top_tags_df, movies[['movieId', 'title','genres']], on='movieId')\n",
    "movies = movies.fillna(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>top_relevance</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[toys, computer animation, pixar animation, ki...</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>[adventure, children, fantasy, kids, special e...</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>[comedy, gunfight, romance, destiny, great]</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>[women, chick flick, divorce, girlie movie, ro...</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 daughter relationship, pregnancy, midl...</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                                      top_relevance  \\\n",
       "0        1  [toys, computer animation, pixar animation, ki...   \n",
       "1        2  [adventure, children, fantasy, kids, special e...   \n",
       "2        3        [comedy, gunfight, romance, destiny, great]   \n",
       "3        4  [women, chick flick, divorce, girlie movie, ro...   \n",
       "4        5  [father daughter relationship, pregnancy, midl...   \n",
       "\n",
       "                                title  \\\n",
       "0                    Toy Story (1995)   \n",
       "1                      Jumanji (1995)   \n",
       "2             Grumpier Old Men (1995)   \n",
       "3            Waiting to Exhale (1995)   \n",
       "4  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genres'] = movies['genres'].apply(lambda x: re.sub(r'\\|', ' ', x).lower())\n",
    "movies['top_relevance'] = movies['top_relevance'].apply(lambda x: ' '.join(x).replace('-',' '))\n",
    "\n",
    "movies['combine_relevant'] = movies['top_relevance']+\" \"+movies['genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>top_relevance</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>combine_relevant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>toys computer animation pixar animation kids a...</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>adventure animation children comedy fantasy</td>\n",
       "      <td>toys computer animation pixar animation kids a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>adventure children fantasy kids special effects</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>adventure children fantasy</td>\n",
       "      <td>adventure children fantasy kids special effect...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>comedy gunfight romance destiny great</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>comedy romance</td>\n",
       "      <td>comedy gunfight romance destiny great comedy r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>women chick flick divorce girlie movie romantic</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>comedy drama romance</td>\n",
       "      <td>women chick flick divorce girlie movie romanti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>father daughter relationship pregnancy midlife...</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>father daughter relationship pregnancy midlife...</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>13811</th>\n",
       "      <td>205072</td>\n",
       "      <td>dumb but funny friendship runaway great movie ...</td>\n",
       "      <td>Zombieland: Double Tap (2019)</td>\n",
       "      <td>action comedy horror</td>\n",
       "      <td>dumb but funny friendship runaway great movie ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13812</th>\n",
       "      <td>205076</td>\n",
       "      <td>girlie movie light feel good oscar (best writi...</td>\n",
       "      <td>Downton Abbey (2019)</td>\n",
       "      <td>drama</td>\n",
       "      <td>girlie movie light feel good oscar (best writi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13813</th>\n",
       "      <td>205383</td>\n",
       "      <td>chase suspense clever drama great ending</td>\n",
       "      <td>El Camino: A Breaking Bad Movie (2019)</td>\n",
       "      <td>crime drama thriller</td>\n",
       "      <td>chase suspense clever drama great ending crime...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13814</th>\n",
       "      <td>205425</td>\n",
       "      <td>stand up comedy comedy highly quotable very fu...</td>\n",
       "      <td>Dave Chappelle: Sticks &amp; Stones (2019)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>stand up comedy comedy highly quotable very fu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13815</th>\n",
       "      <td>206499</td>\n",
       "      <td>pg 13 light road trip pornography justice</td>\n",
       "      <td>Between Two Ferns: The Movie (2019)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>pg 13 light road trip pornography justice comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13816 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                      top_relevance  \\\n",
       "0            1  toys computer animation pixar animation kids a...   \n",
       "1            2    adventure children fantasy kids special effects   \n",
       "2            3              comedy gunfight romance destiny great   \n",
       "3            4    women chick flick divorce girlie movie romantic   \n",
       "4            5  father daughter relationship pregnancy midlife...   \n",
       "...        ...                                                ...   \n",
       "13811   205072  dumb but funny friendship runaway great movie ...   \n",
       "13812   205076  girlie movie light feel good oscar (best writi...   \n",
       "13813   205383           chase suspense clever drama great ending   \n",
       "13814   205425  stand up comedy comedy highly quotable very fu...   \n",
       "13815   206499          pg 13 light road trip pornography justice   \n",
       "\n",
       "                                        title  \\\n",
       "0                            Toy Story (1995)   \n",
       "1                              Jumanji (1995)   \n",
       "2                     Grumpier Old Men (1995)   \n",
       "3                    Waiting to Exhale (1995)   \n",
       "4          Father of the Bride Part II (1995)   \n",
       "...                                       ...   \n",
       "13811           Zombieland: Double Tap (2019)   \n",
       "13812                    Downton Abbey (2019)   \n",
       "13813  El Camino: A Breaking Bad Movie (2019)   \n",
       "13814  Dave Chappelle: Sticks & Stones (2019)   \n",
       "13815     Between Two Ferns: The Movie (2019)   \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",
       "13811                         action comedy horror   \n",
       "13812                                        drama   \n",
       "13813                         crime drama thriller   \n",
       "13814                                       comedy   \n",
       "13815                                       comedy   \n",
       "\n",
       "                                        combine_relevant  \n",
       "0      toys computer animation pixar animation kids a...  \n",
       "1      adventure children fantasy kids special effect...  \n",
       "2      comedy gunfight romance destiny great comedy r...  \n",
       "3      women chick flick divorce girlie movie romanti...  \n",
       "4      father daughter relationship pregnancy midlife...  \n",
       "...                                                  ...  \n",
       "13811  dumb but funny friendship runaway great movie ...  \n",
       "13812  girlie movie light feel good oscar (best writi...  \n",
       "13813  chase suspense clever drama great ending crime...  \n",
       "13814  stand up comedy comedy highly quotable very fu...  \n",
       "13815   pg 13 light road trip pornography justice comedy  \n",
       "\n",
       "[13816 rows x 5 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommenadtion Algorithm Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "counter_vec = CountVectorizer(stop_words='english',max_features=1500)\n",
    "counter_vec.fit(movies['combine_relevant'])\n",
    "\n",
    "gerne_vec_tags = counter_vec.transform(movies['top_relevance'])*0.3 \n",
    "gerne_vec_geners = counter_vec.transform(movies['genres'])*1 \n",
    "\n",
    "gerne_vec = gerne_vec_tags+gerne_vec_geners\n",
    "\n",
    "\n",
    "cos_similar = cosine_similarity(gerne_vec,gerne_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13816, 13816)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos_similar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_title_series = pd.Series(movies.index,movies['title'])\n",
    "\n",
    "# Get list of 1 movie \n",
    "def get_recommend(title,cosine_sim = cos_similar, length = 10):\n",
    "    movie_name = movie_title_series[title]\n",
    "    \n",
    "    sim_scores = list(enumerate(cosine_sim[movie_name]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:length+1]\n",
    "\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "\n",
    "    return movies['title'].iloc[movie_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9966                                Zombieland (2009)\n",
       "11768                               Zombeavers (2014)\n",
       "12548    Scouts Guide to the Zombie Apocalypse (2015)\n",
       "2934                  Buffy the Vampire Slayer (1992)\n",
       "11957               Dead Snow 2: Red vs. Dead (2014) \n",
       "13476                               Game Night (2018)\n",
       "6779       Citizen Toxie: The Toxic Avenger IV (2000)\n",
       "6543                                    Versus (2000)\n",
       "8682                                     Feast (2005)\n",
       "1923                       Surf Nazis Must Die (1987)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommend('Zombieland: Double Tap (2019)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_list_movie=['Zombieland: Double Tap (2019)','Zombieland (2009)','Shaun of the Dead (2004)','Cockneys vs Zombies (2012)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "def get_recommend_by_user_list(user_list, length=10):\n",
    "    feature_names = list(counter_vec.vocabulary_.keys())\n",
    " \n",
    "    user_vec = np.zeros((1, len(feature_names)))\n",
    "   \n",
    "    for movie_name in user_list:\n",
    "        vector_tmp = counter_vec.transform(movies[movies['title'] == movie_name]['top_relevance'])*0.3 \n",
    "        vector_tmp += counter_vec.transform(movies[movies['title'] == movie_name]['genres'])*1\n",
    "        user_vec += vector_tmp.toarray()\n",
    "\n",
    "\n",
    "    cosine = cosine_similarity(user_vec, gerne_vec)\n",
    "\n",
    "    sim_scores = list(enumerate(cosine[0]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[len(user_list):len(user_list)+length+1]\n",
    "    \n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "\n",
    "    return movies['title'].iloc[movie_indices]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11258    Juan of the Dead (Juan de los Muertos) (2011)\n",
       "10499                     Tucker & Dale vs Evil (2010)\n",
       "11957                Dead Snow 2: Red vs. Dead (2014) \n",
       "11768                                Zombeavers (2014)\n",
       "4075          Return of the Living Dead Part II (1988)\n",
       "10053                                  Doghouse (2009)\n",
       "12563                                   Cooties (2015)\n",
       "2327                                 Idle Hands (1999)\n",
       "12468                            Burying the Ex (2015)\n",
       "13364                               Little Evil (2017)\n",
       "6393                                 Club Dread (2004)\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommend_by_user_list(user_list_movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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