004. recommender system basic with Python - 3 Matrix Factorization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 블로그 설명\n",
    "\n",
    "해당 자료에 대한 설명은 아래 블로그에도 올려두었습니다.\n",
    "- https://lsjsj92.tistory.com/569\n",
    "\n",
    "---- \n",
    "\n",
    "# 추천 시스템 - 협업 필터링 Matrix Factorization\n",
    "\n",
    "이번 글은 추천 시스템의 협업 필터링 중에서 행렬 분해(Matrix Factorization)에 대해서 작성합니다.  \n",
    "해당 자료는 아래에서 참고했습니다.\n",
    "\n",
    "- https://github.com/SurhanZahid/Recommendation-System-Using-Matrix-Factorization/blob/master/Recommender%20System%20With%20Matrix%20Factorization%20.ipynb\n",
    "- https://github.com/nikitaa30/Recommender-Systems/blob/master/matrix_factorisation_svd.py\n",
    "\n",
    "데이터는 kaggle의 https://www.kaggle.com/sengzhaotoo/movielens-small 이 데이터입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "from scipy.sparse.linalg import svds\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rating_data = pd.read_csv('./datas/movie_lens/ratings.csv')\n",
    "movie_data = pd.read_csv('./datas/movie_lens/movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </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",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9125, 3)\n",
      "(100004, 4)\n"
     ]
    }
   ],
   "source": [
    "print(movie_data.shape)\n",
    "print(rating_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "두 개의 파일은 사용자-평점 데이터와 영화 데이터로 나뉘어져 있습니다.   \n",
    "\n",
    "이 두개의 파일은 공통적으로 movieId를 가지고 있습니다. 즉, movieId를 이용하면 하나로 합칠 수 있습니다.  \n",
    "\n",
    "\n",
    "# 전처리 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1       31     2.5\n",
       "1       1     1029     3.0\n",
       "2       1     1061     3.0\n",
       "3       1     1129     2.0\n",
       "4       1     1172     4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_data.drop('timestamp', axis = 1, inplace = True)\n",
    "rating_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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",
       "    </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>Jumanji (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   movieId                               title\n",
       "0        1                    Toy Story (1995)\n",
       "1        2                      Jumanji (1995)\n",
       "2        3             Grumpier Old Men (1995)\n",
       "3        4            Waiting to Exhale (1995)\n",
       "4        5  Father of the Bride Part II (1995)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data.drop('genres', axis = 1, inplace = True)\n",
    "movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dangerous Minds (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating                   title\n",
       "0       1       31     2.5  Dangerous Minds (1995)\n",
       "1       7       31     3.0  Dangerous Minds (1995)\n",
       "2      31       31     4.0  Dangerous Minds (1995)\n",
       "3      32       31     4.0  Dangerous Minds (1995)\n",
       "4      36       31     3.0  Dangerous Minds (1995)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_data = pd.merge(rating_data, movie_data, on = 'movieId')\n",
    "user_movie_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100004, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "불필요한 컬럼을 제거한 뒤 pandas의 merge를 이용해서 하나로 합쳤습니다.   \n",
    "\n",
    "이제, pivot_table을 만들어줍니다.   \n",
    "\n",
    "value에는 rating값을, column에는 movie title 값을, index는 user id를 넣어줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_movie_rating = user_movie_data.pivot_table('rating', index = 'userId', columns='title').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9064)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>\"Great Performances\" Cats (1998)</th>\n",
       "      <th>$9.99 (2008)</th>\n",
       "      <th>'Hellboy': The Seeds of Creation (2004)</th>\n",
       "      <th>'Neath the Arizona Skies (1934)</th>\n",
       "      <th>'Round Midnight (1986)</th>\n",
       "      <th>'Salem's Lot (2004)</th>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <th>'night Mother (1986)</th>\n",
       "      <th>(500) Days of Summer (2009)</th>\n",
       "      <th>...</th>\n",
       "      <th>Zulu (1964)</th>\n",
       "      <th>Zulu (2013)</th>\n",
       "      <th>[REC] (2007)</th>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <th>loudQUIETloud: A Film About the Pixies (2006)</th>\n",
       "      <th>xXx (2002)</th>\n",
       "      <th>xXx: State of the Union (2005)</th>\n",
       "      <th>¡Three Amigos! (1986)</th>\n",
       "      <th>À nous la liberté (Freedom for Us) (1931)</th>\n",
       "      <th>İtirazım Var (2014)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9064 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   \"Great Performances\" Cats (1998)  $9.99 (2008)  \\\n",
       "userId                                                   \n",
       "1                                    0.0           0.0   \n",
       "2                                    0.0           0.0   \n",
       "3                                    0.0           0.0   \n",
       "4                                    0.0           0.0   \n",
       "5                                    0.0           0.0   \n",
       "\n",
       "title   'Hellboy': The Seeds of Creation (2004)  \\\n",
       "userId                                            \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           0.0   \n",
       "4                                           0.0   \n",
       "5                                           0.0   \n",
       "\n",
       "title   'Neath the Arizona Skies (1934)  'Round Midnight (1986)  \\\n",
       "userId                                                            \n",
       "1                                   0.0                     0.0   \n",
       "2                                   0.0                     0.0   \n",
       "3                                   0.0                     0.0   \n",
       "4                                   0.0                     0.0   \n",
       "5                                   0.0                     0.0   \n",
       "\n",
       "title   'Salem's Lot (2004)  'Til There Was You (1997)  'burbs, The (1989)  \\\n",
       "userId                                                                       \n",
       "1                       0.0                        0.0                 0.0   \n",
       "2                       0.0                        0.0                 0.0   \n",
       "3                       0.0                        0.0                 0.0   \n",
       "4                       0.0                        0.0                 0.0   \n",
       "5                       0.0                        0.0                 0.0   \n",
       "\n",
       "title   'night Mother (1986)  (500) Days of Summer (2009)  ...  Zulu (1964)  \\\n",
       "userId                                                     ...                \n",
       "1                        0.0                          0.0  ...          0.0   \n",
       "2                        0.0                          0.0  ...          0.0   \n",
       "3                        0.0                          0.0  ...          0.0   \n",
       "4                        0.0                          0.0  ...          0.0   \n",
       "5                        0.0                          0.0  ...          0.0   \n",
       "\n",
       "title   Zulu (2013)  [REC] (2007)  eXistenZ (1999)  \\\n",
       "userId                                               \n",
       "1               0.0           0.0              0.0   \n",
       "2               0.0           0.0              0.0   \n",
       "3               0.0           0.0              0.0   \n",
       "4               0.0           0.0              0.0   \n",
       "5               0.0           0.0              0.0   \n",
       "\n",
       "title   loudQUIETloud: A Film About the Pixies (2006)  xXx (2002)  \\\n",
       "userId                                                              \n",
       "1                                                 0.0         0.0   \n",
       "2                                                 0.0         0.0   \n",
       "3                                                 0.0         0.0   \n",
       "4                                                 0.0         0.0   \n",
       "5                                                 0.0         0.0   \n",
       "\n",
       "title   xXx: State of the Union (2005)  ¡Three Amigos! (1986)  \\\n",
       "userId                                                          \n",
       "1                                  0.0                    0.0   \n",
       "2                                  0.0                    0.0   \n",
       "3                                  0.0                    0.0   \n",
       "4                                  0.0                    0.0   \n",
       "5                                  0.0                    0.0   \n",
       "\n",
       "title   À nous la liberté (Freedom for Us) (1931)  İtirazım Var (2014)  \n",
       "userId                                                                  \n",
       "1                                             0.0                  0.0  \n",
       "2                                             0.0                  0.0  \n",
       "3                                             0.0                  0.0  \n",
       "4                                             0.0                  0.0  \n",
       "5                                             0.0                  0.0  \n",
       "\n",
       "[5 rows x 9064 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_movie_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "자! 이제 사용자-영화 데이터의 pivot table을 만들었습니다.  \n",
    "\n",
    "이제 사용자-영화 기준의 데이터를 **영화-사용자** 기준으로 만들어서 특정 '영화'와 비슷한 영화를 추천해주는 로직을 구현해봅니다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9064, 671)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_user_rating = user_movie_rating.values.T\n",
    "movie_user_rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(movie_user_rating)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기서 이제 SVD를 사용합니다.   \n",
    "\n",
    "**SVD(Singular Value Decomposion), 특이값 분해**란 m x n 크기의 데이터 행렬 A를 아래와 같이 분해하는 것을 말합니다.\n",
    "\n",
    "![15](https://user-images.githubusercontent.com/24634054/73115129-93138c00-3f65-11ea-9a10-80abc59a8494.JPG)\n",
    "\n",
    "\n",
    "출처 : https://ratsgo.github.io/from%20frequency%20to%20semantics/2017/04/06/pcasvdlsa/\n",
    "\n",
    "행렬 U와 V에 속한 열벡터는 특이벡터(singular vector)로 불리고, 이 특이벡터들은 서로 직교하는 성질을 가지고 있습니다.\n",
    "또한, 가운데 시그마 기호로 보이는 것은 이것도 행렬인데요. 이 행렬은 대각행렬(diagonal matrix) 성질을 가지고 있습니다. 그래서 대각 성분이 행렬 A의 특이값이고 나머지 성분이 0입니다.\n",
    "\n",
    "\n",
    "그리고 사이킷런에서 제공해주는 truncated SVD는 이러한 SVD의 변형입니다.\n",
    "**truncated SVD**는 시그마 행렬의 대각원소(특이값) 가운데 상위 n개만 골라낸 것입니다. 이렇게 하면 기존 행렬 A의 성질을 100% 원복할 수는 없지만, (그 만큼 데이터 정보를 압축) 행렬 A와 거의 근사한 값이 나오게 됩니다.\n",
    "\n",
    "\n",
    "\n",
    "여기서는 scikit learn의 TruncatedSVD를 사용해봅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9064, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVD = TruncatedSVD(n_components=12)\n",
    "matrix = SVD.fit_transform(movie_user_rating)\n",
    "matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01227491,  0.00250724,  0.01554347, -0.03396397, -0.01454107,\n",
       "        0.00372211, -0.0019783 ,  0.04563075, -0.01597626, -0.02075279,\n",
       "        0.00934806, -0.01379995])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12개의 component로 차원을 축소했습니다. \n",
    "\n",
    "이제 이렇게 나온 데이터를 활용해서 피어슨 상관계수를 구합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9064, 9064)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = np.corrcoef(matrix)\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 200)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr2 = corr[:200, :200]\n",
    "corr2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b4f9739be0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "sns.heatmap(corr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 나온 상관계수를 이용해서 특정 영화와 상관계수가 높은 영화를 뽑아줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_title = user_movie_rating.columns\n",
    "movie_title_list = list(movie_title)\n",
    "coffey_hands = movie_title_list.index(\"Guardians of the Galaxy (2014)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2 Guns (2013)',\n",
       " '2012 (2009)',\n",
       " '21 (2008)',\n",
       " '300: Rise of an Empire (2014)',\n",
       " 'Abduction (2011)',\n",
       " 'Adjustment Bureau, The (2011)',\n",
       " 'Adventures of Tintin, The (2011)',\n",
       " 'Alice in Wonderland (2010)',\n",
       " 'Amazing Spider-Man, The (2012)',\n",
       " 'Angels & Demons (2009)',\n",
       " 'Ant-Man (2015)',\n",
       " 'Avatar (2009)',\n",
       " 'Avengers, The (2012)',\n",
       " 'Avengers: Age of Ultron (2015)',\n",
       " 'Batman: The Dark Knight Returns, Part 1 (2012)',\n",
       " 'Batman: The Dark Knight Returns, Part 2 (2013)',\n",
       " 'Battle: Los Angeles (2011)',\n",
       " 'Bee Movie (2007)',\n",
       " 'Beowulf (2007)',\n",
       " 'Big Hero 6 (2014)',\n",
       " 'Bolt (2008)',\n",
       " 'Brave (2012)',\n",
       " 'Captain America: Civil War (2016)',\n",
       " 'Captain America: The First Avenger (2011)',\n",
       " 'Captain America: The Winter Soldier (2014)',\n",
       " 'Chronicle (2012)',\n",
       " 'Click (2006)',\n",
       " 'Cloverfield (2008)',\n",
       " 'Cop Out (2010)',\n",
       " 'Crank: High Voltage (2009)',\n",
       " 'Dark Knight Rises, The (2012)',\n",
       " 'Diary of a Wimpy Kid (2010)',\n",
       " 'District 9 (2009)',\n",
       " 'Dredd (2012)',\n",
       " 'Drillbit Taylor (2008)',\n",
       " 'Déjà Vu (Deja Vu) (2006)',\n",
       " 'Edge of Tomorrow (2014)',\n",
       " 'Elysium (2013)',\n",
       " \"Ender's Game (2013)\",\n",
       " 'Ex Machina (2015)',\n",
       " 'Fright Night (2011)',\n",
       " 'Fury (2014)',\n",
       " 'Get Smart (2008)',\n",
       " 'Ghost Rider: Spirit of Vengeance (2012)',\n",
       " 'Girl with the Dragon Tattoo, The (2011)',\n",
       " 'Godzilla (2014)',\n",
       " 'Gravity (2013)',\n",
       " 'Green Hornet, The (2011)',\n",
       " 'Green Lantern (2011)',\n",
       " 'Guardians of the Galaxy (2014)']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_coffey_hands  = corr[coffey_hands]\n",
    "list(movie_title[(corr_coffey_hands >= 0.9)])[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**마블의 가디언즈 오브 갤럭시** 영화를 기준으로 비슷한 영화를 뽑아봤습니다.   \n",
    "10000BC, 엔트맨, 어벤저스 영화 등이 보이는 것을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 한 사용자에게 개인 추천을 해주기\n",
    "\n",
    "위에서는 하나의 영화에 대해서 비슷한 영화를 추천해주는 것을 적용했습니다.   \n",
    "하지만, 보통 추천 시스템은 사용자에게 추천을 해주어야 합니다.  \n",
    "\n",
    "사용자에게 추천을 해주기 위해서 사용자 맞춤 협업 필터링 행렬 분해를 적용해보겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings  = pd.read_csv('./datas/movie_lens/ratings.csv')\n",
    "df_movies  = pd.read_csv('./datas/movie_lens/movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_user_movie_ratings = df_ratings.pivot(\n",
    "    index='userId',\n",
    "    columns='movieId',\n",
    "    values='rating'\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>...</th>\n",
       "      <th>161084</th>\n",
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       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId  1       2       3       4       5       6       7       8       \\\n",
       "userId                                                                    \n",
       "1           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4           0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "5           0.0     0.0     4.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  9       10      ...  161084  161155  161594  161830  161918  161944  \\\n",
       "userId                   ...                                                   \n",
       "1           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2           0.0     4.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "3           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "4           0.0     4.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "5           0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "movieId  162376  162542  162672  163949  \n",
       "userId                                   \n",
       "1           0.0     0.0     0.0     0.0  \n",
       "2           0.0     0.0     0.0     0.0  \n",
       "3           0.0     0.0     0.0     0.0  \n",
       "4           0.0     0.0     0.0     0.0  \n",
       "5           0.0     0.0     0.0     0.0  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_user_movie_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기까지는 앞서 했던 것과 똑같습니다. 사용자-영화 pivot table을 만드는 것이죠.   \n",
    "\n",
    "이제 아래와 같이 데이터를 조금 변경해서 진행하겠습니다.\n",
    "\n",
    "1. pivot table을 matrix로 변환\n",
    "2. np.mean(axis = 1)을 통해 각 사용자들이 매기는 평점 평균을 구함\n",
    "3. 1에서 구한 값과 2에서 구한 값을 빼서 사용자-평균 데이터 값을 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix는 pivot_table 값을 numpy matrix로 만든 것 \n",
    "matrix = df_user_movie_ratings.as_matrix()\n",
    "\n",
    "# user_ratings_mean은 사용자의 평균 평점 \n",
    "user_ratings_mean = np.mean(matrix, axis = 1)\n",
    "\n",
    "# R_user_mean : 사용자-영화에 대해 사용자 평균 평점을 뺀 것.\n",
    "matrix_user_mean = matrix - user_ratings_mean.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [4., 0., 0., ..., 0., 0., 0.],\n",
       "       [5., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ratings_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_user_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "      <td>-0.005625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>3.970770</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "      <td>-0.029230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "      <td>-0.020075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>3.902162</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "      <td>-0.097838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>3.956872</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "      <td>-0.043128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId    1         2         3         4         5         6         7       \\\n",
       "0       -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625   \n",
       "1       -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230   \n",
       "2       -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075   \n",
       "3       -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838   \n",
       "4       -0.043128 -0.043128  3.956872 -0.043128 -0.043128 -0.043128 -0.043128   \n",
       "\n",
       "movieId    8         9         10      ...    161084    161155    161594  \\\n",
       "0       -0.005625 -0.005625 -0.005625  ... -0.005625 -0.005625 -0.005625   \n",
       "1       -0.029230 -0.029230  3.970770  ... -0.029230 -0.029230 -0.029230   \n",
       "2       -0.020075 -0.020075 -0.020075  ... -0.020075 -0.020075 -0.020075   \n",
       "3       -0.097838 -0.097838  3.902162  ... -0.097838 -0.097838 -0.097838   \n",
       "4       -0.043128 -0.043128 -0.043128  ... -0.043128 -0.043128 -0.043128   \n",
       "\n",
       "movieId    161830    161918    161944    162376    162542    162672    163949  \n",
       "0       -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625 -0.005625  \n",
       "1       -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230 -0.029230  \n",
       "2       -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075 -0.020075  \n",
       "3       -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838 -0.097838  \n",
       "4       -0.043128 -0.043128 -0.043128 -0.043128 -0.043128 -0.043128 -0.043128  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(matrix_user_mean, columns = df_user_movie_ratings.columns).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여기까지 진행하면 초기에 만들었던 user-movie pivot table 값이 matrix_user_mean 변경되었습니다.\n",
    "\n",
    "즉, 아래와 같이 변경된 것이죠.\n",
    "\n",
    "1. 사용자 671명이 9066개의 영화에 대해 평점을 매긴 값이 존재\n",
    "2. 사용자 671명의 각각 평균 평점을 구해서\n",
    "3. 사용자 671명의 9066개의 영화에 대해 평점을 조금 변경 -> 1에서 구한 값 - 2에서 구한 값\n",
    "\n",
    "\n",
    "**이제 SVD를 이용해 Matrix Factorization**을 진행해봅니다. \n",
    "\n",
    "앞서서는 scikit learn을 이용해 TruncatedSVD를 이용했는데요. 이번에는 scipy를 이용해 Truncated SVD를 구해봅니다.\n",
    "\n",
    "이 둘의 차이점은 scikit learn에서 제공해주는 TruncatedSVD는 U, Sigma, Vt 반환 값을 제공하지 않습니다.   \n",
    "하지만, Scipy를 이용하면 이 반환값들을 제공받을 수 있죠.\n",
    "\n",
    "Scipy에서 제공해주는 Truncate SVD는 scipy.sparse.linalg.svds를 이용하면 됩니다. 저는 이것을 이용했습니다.\n",
    "\n",
    "이렇게 사용하면 반환값이 U 행렬, Sigma 행렬, V 전치 행렬(Vt)가 나오게 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scipy에서 제공해주는 svd.  \n",
    "# U 행렬, sigma 행렬, V 전치 행렬을 반환.\n",
    "\n",
    "U, sigma, Vt = svds(matrix_user_mean, k = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(671, 12)\n",
      "(12,)\n",
      "(12, 9066)\n"
     ]
    }
   ],
   "source": [
    "print(U.shape)\n",
    "print(sigma.shape)\n",
    "print(Vt.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "현재 이 Sigma 행렬은 0이 아닌 값만 1차원 행렬로 표현된 상태입니다.  \n",
    "**즉, 0이 포함된 대칭행렬로 변환할 때는 numpy의 diag를 이용해야 합니다.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = np.diag(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 12)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([105.72437051,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        , 110.15522471,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 대칭 행렬로 변환이 되었습니다.   \n",
    "\n",
    "현재 까지 상황을 정리하면 아래와 같습니다.   \n",
    "\n",
    "1. 원본 user-movie 평점 행렬이 있었음\n",
    "2. 이를 user의 평균 점수를 빼서 matrix_user_mean 이라는 행렬로 만듬\n",
    "3. 2번의 값을 SVD를 적용해 U, Sigma, Vt 행렬을 구했음\n",
    "4. Sigma 행렬은 현재 0이 포함이 되지 않은 값으로만 구성되어 있음. 이를 대칭행렬로 변환\n",
    "\n",
    "\n",
    "자! 이제 여기서 matrix_user_mean을 SVD를 적용해 분해를 한 상태입니다.  \n",
    "이제, 다시 원본 행렬로 복구시켜야겠죠?\n",
    "\n",
    "원본 행렬로 복구시키는 방법은 아래와 같습니다.\n",
    "\n",
    "- U, Sigma, Vt의 내적을 수행 \n",
    "\n",
    "즉, np.dot(np.dot(U, sigma), Vt)를 수행하면 됩니다. \n",
    "\n",
    "그리고 아까 사용자 평균을 빼주었으니 여기서는 더해줍니다. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# U, Sigma, Vt의 내적을 수행하면, 다시 원본 행렬로 복원이 된다. \n",
    "# 거기에 + 사용자 평균 rating을 적용한다. \n",
    "svd_user_predicted_ratings = np.dot(np.dot(U, sigma), Vt) + user_ratings_mean.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>movieId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>161084</th>\n",
       "      <th>161155</th>\n",
       "      <th>161594</th>\n",
       "      <th>161830</th>\n",
       "      <th>161918</th>\n",
       "      <th>161944</th>\n",
       "      <th>162376</th>\n",
       "      <th>162542</th>\n",
       "      <th>162672</th>\n",
       "      <th>163949</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.079686</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>-0.013837</td>\n",
       "      <td>-0.005870</td>\n",
       "      <td>-0.028877</td>\n",
       "      <td>0.032371</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>-0.004428</td>\n",
       "      <td>-0.005219</td>\n",
       "      <td>0.038195</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004324</td>\n",
       "      <td>-0.004352</td>\n",
       "      <td>0.010478</td>\n",
       "      <td>-0.004256</td>\n",
       "      <td>-0.003944</td>\n",
       "      <td>-0.005674</td>\n",
       "      <td>0.018157</td>\n",
       "      <td>-0.005575</td>\n",
       "      <td>-0.005297</td>\n",
       "      <td>-0.003766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.428452</td>\n",
       "      <td>1.608841</td>\n",
       "      <td>0.529476</td>\n",
       "      <td>0.168278</td>\n",
       "      <td>0.520809</td>\n",
       "      <td>1.107473</td>\n",
       "      <td>0.529719</td>\n",
       "      <td>0.089376</td>\n",
       "      <td>0.296270</td>\n",
       "      <td>1.970031</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013227</td>\n",
       "      <td>-0.002275</td>\n",
       "      <td>0.020680</td>\n",
       "      <td>-0.005245</td>\n",
       "      <td>-0.007644</td>\n",
       "      <td>-0.021019</td>\n",
       "      <td>0.031243</td>\n",
       "      <td>-0.000957</td>\n",
       "      <td>-0.000753</td>\n",
       "      <td>0.026901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.977246</td>\n",
       "      <td>0.396971</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.027444</td>\n",
       "      <td>0.021287</td>\n",
       "      <td>0.141458</td>\n",
       "      <td>-0.057134</td>\n",
       "      <td>0.031633</td>\n",
       "      <td>-0.012538</td>\n",
       "      <td>0.383576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.004907</td>\n",
       "      <td>-0.014190</td>\n",
       "      <td>-0.000251</td>\n",
       "      <td>-0.006007</td>\n",
       "      <td>-0.003189</td>\n",
       "      <td>-0.026916</td>\n",
       "      <td>0.014637</td>\n",
       "      <td>0.013287</td>\n",
       "      <td>-0.005741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.870844</td>\n",
       "      <td>1.169993</td>\n",
       "      <td>0.252202</td>\n",
       "      <td>0.094831</td>\n",
       "      <td>-0.181713</td>\n",
       "      <td>-0.511953</td>\n",
       "      <td>-0.027820</td>\n",
       "      <td>-0.143080</td>\n",
       "      <td>0.013247</td>\n",
       "      <td>1.461694</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026412</td>\n",
       "      <td>-0.027245</td>\n",
       "      <td>0.054681</td>\n",
       "      <td>0.018450</td>\n",
       "      <td>0.034544</td>\n",
       "      <td>-0.035740</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>-0.019365</td>\n",
       "      <td>-0.017113</td>\n",
       "      <td>0.066559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.182777</td>\n",
       "      <td>0.924903</td>\n",
       "      <td>0.075998</td>\n",
       "      <td>0.061505</td>\n",
       "      <td>0.602680</td>\n",
       "      <td>-0.159825</td>\n",
       "      <td>0.339925</td>\n",
       "      <td>0.081534</td>\n",
       "      <td>-0.079666</td>\n",
       "      <td>0.535018</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029124</td>\n",
       "      <td>-0.029357</td>\n",
       "      <td>0.009064</td>\n",
       "      <td>-0.029092</td>\n",
       "      <td>-0.030890</td>\n",
       "      <td>-0.057453</td>\n",
       "      <td>0.026344</td>\n",
       "      <td>-0.024027</td>\n",
       "      <td>-0.024614</td>\n",
       "      <td>-0.032752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 9066 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "movieId    1         2         3         4         5         6         7       \\\n",
       "0       -0.079686  0.021779 -0.013837 -0.005870 -0.028877  0.032371  0.000715   \n",
       "1        1.428452  1.608841  0.529476  0.168278  0.520809  1.107473  0.529719   \n",
       "2        0.977246  0.396971  0.000299  0.027444  0.021287  0.141458 -0.057134   \n",
       "3        1.870844  1.169993  0.252202  0.094831 -0.181713 -0.511953 -0.027820   \n",
       "4        1.182777  0.924903  0.075998  0.061505  0.602680 -0.159825  0.339925   \n",
       "\n",
       "movieId    8         9         10      ...    161084    161155    161594  \\\n",
       "0       -0.004428 -0.005219  0.038195  ... -0.004324 -0.004352  0.010478   \n",
       "1        0.089376  0.296270  1.970031  ...  0.013227 -0.002275  0.020680   \n",
       "2        0.031633 -0.012538  0.383576  ...  0.002761  0.004907 -0.014190   \n",
       "3       -0.143080  0.013247  1.461694  ...  0.026412 -0.027245  0.054681   \n",
       "4        0.081534 -0.079666  0.535018  ... -0.029124 -0.029357  0.009064   \n",
       "\n",
       "movieId    161830    161918    161944    162376    162542    162672    163949  \n",
       "0       -0.004256 -0.003944 -0.005674  0.018157 -0.005575 -0.005297 -0.003766  \n",
       "1       -0.005245 -0.007644 -0.021019  0.031243 -0.000957 -0.000753  0.026901  \n",
       "2       -0.000251 -0.006007 -0.003189 -0.026916  0.014637  0.013287 -0.005741  \n",
       "3        0.018450  0.034544 -0.035740  0.088889 -0.019365 -0.017113  0.066559  \n",
       "4       -0.029092 -0.030890 -0.057453  0.026344 -0.024027 -0.024614 -0.032752  \n",
       "\n",
       "[5 rows x 9066 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_svd_preds = pd.DataFrame(svd_user_predicted_ratings, columns = df_user_movie_ratings.columns)\n",
    "df_svd_preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(671, 9066)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_svd_preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "자! 이제 함수를 하나 만듭니다. 이 함수의 기능은 아래와 같습니다.\n",
    "\n",
    "- 인자로 사용자 아이디, 영화 정보 테이블, 평점 테이블 등을 받음\n",
    "- 사용자 아이디에 SVD로 나온 결과의 영화 평점이 가장 높은 데이터 순으로 정렬\n",
    "- 사용자가 본 데이터를 제외\n",
    "- 사용자가 안 본 영화에서 평점이 높은 것을 추천"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_movies(df_svd_preds, user_id, ori_movies_df, ori_ratings_df, num_recommendations=5):\n",
    "    \n",
    "    #현재는 index로 적용이 되어있으므로 user_id - 1을 해야함.\n",
    "    user_row_number = user_id - 1 \n",
    "    \n",
    "    # 최종적으로 만든 pred_df에서 사용자 index에 따라 영화 데이터 정렬 -> 영화 평점이 높은 순으로 정렬 됌\n",
    "    sorted_user_predictions = df_svd_preds.iloc[user_row_number].sort_values(ascending=False)\n",
    "    \n",
    "    # 원본 평점 데이터에서 user id에 해당하는 데이터를 뽑아낸다. \n",
    "    user_data = ori_ratings_df[ori_ratings_df.userId == user_id]\n",
    "    \n",
    "    # 위에서 뽑은 user_data와 원본 영화 데이터를 합친다. \n",
    "    user_history = user_data.merge(ori_movies_df, on = 'movieId').sort_values(['rating'], ascending=False)\n",
    "    \n",
    "    # 원본 영화 데이터에서 사용자가 본 영화 데이터를 제외한 데이터를 추출\n",
    "    recommendations = ori_movies_df[~ori_movies_df['movieId'].isin(user_history['movieId'])]\n",
    "    # 사용자의 영화 평점이 높은 순으로 정렬된 데이터와 위 recommendations을 합친다. \n",
    "    recommendations = recommendations.merge( pd.DataFrame(sorted_user_predictions).reset_index(), on = 'movieId')\n",
    "    # 컬럼 이름 바꾸고 정렬해서 return\n",
    "    recommendations = recommendations.rename(columns = {user_row_number: 'Predictions'}).sort_values('Predictions', ascending = False).iloc[:num_recommendations, :]\n",
    "                      \n",
    "\n",
    "    return user_history, recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "already_rated, predictions = recommend_movies(df_svd_preds, 330, df_movies, df_ratings, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>330</td>\n",
       "      <td>1094</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577200</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>330</td>\n",
       "      <td>213</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577039</td>\n",
       "      <td>Burnt by the Sun (Utomlyonnye solntsem) (1994)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>330</td>\n",
       "      <td>527</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577164</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>330</td>\n",
       "      <td>1035</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948574126</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>330</td>\n",
       "      <td>1172</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948575236</td>\n",
       "      <td>Cinema Paradiso (Nuovo cinema Paradiso) (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>330</td>\n",
       "      <td>1249</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948576522</td>\n",
       "      <td>Femme Nikita, La (Nikita) (1990)</td>\n",
       "      <td>Action|Crime|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>330</td>\n",
       "      <td>373</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948576522</td>\n",
       "      <td>Red Rock West (1992)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>330</td>\n",
       "      <td>1428</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948731410</td>\n",
       "      <td>Angel Baby (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>330</td>\n",
       "      <td>1575</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948577442</td>\n",
       "      <td>Gabbeh (1996)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>330</td>\n",
       "      <td>1683</td>\n",
       "      <td>5.0</td>\n",
       "      <td>948578017</td>\n",
       "      <td>Wings of the Dove, The (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId  rating  timestamp  \\\n",
       "47     330     1094     5.0  948577200   \n",
       "14     330      213     5.0  948577039   \n",
       "34     330      527     5.0  948577164   \n",
       "45     330     1035     5.0  948574126   \n",
       "48     330     1172     5.0  948575236   \n",
       "53     330     1249     5.0  948576522   \n",
       "27     330      373     5.0  948576522   \n",
       "58     330     1428     5.0  948731410   \n",
       "61     330     1575     5.0  948577442   \n",
       "67     330     1683     5.0  948578017   \n",
       "\n",
       "                                             title  \\\n",
       "47                         Crying Game, The (1992)   \n",
       "14  Burnt by the Sun (Utomlyonnye solntsem) (1994)   \n",
       "34                         Schindler's List (1993)   \n",
       "45                      Sound of Music, The (1965)   \n",
       "48  Cinema Paradiso (Nuovo cinema Paradiso) (1989)   \n",
       "53                Femme Nikita, La (Nikita) (1990)   \n",
       "27                            Red Rock West (1992)   \n",
       "58                               Angel Baby (1995)   \n",
       "61                                   Gabbeh (1996)   \n",
       "67                   Wings of the Dove, The (1997)   \n",
       "\n",
       "                           genres  \n",
       "47         Drama|Romance|Thriller  \n",
       "14                          Drama  \n",
       "34                      Drama|War  \n",
       "45                Musical|Romance  \n",
       "48                          Drama  \n",
       "53  Action|Crime|Romance|Thriller  \n",
       "27                       Thriller  \n",
       "58                          Drama  \n",
       "61                          Drama  \n",
       "67                  Drama|Romance  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "already_rated.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>296</td>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>2.060064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>356</td>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>Comedy|Drama|Romance|War</td>\n",
       "      <td>1.772814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>608</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Comedy|Crime|Drama|Thriller</td>\n",
       "      <td>1.526111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>590</td>\n",
       "      <td>Dances with Wolves (1990)</td>\n",
       "      <td>Adventure|Drama|Western</td>\n",
       "      <td>1.508960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>380</td>\n",
       "      <td>True Lies (1994)</td>\n",
       "      <td>Action|Adventure|Comedy|Romance|Thriller</td>\n",
       "      <td>1.276646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>47</td>\n",
       "      <td>Seven (a.k.a. Se7en) (1995)</td>\n",
       "      <td>Mystery|Thriller</td>\n",
       "      <td>1.265219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>377</td>\n",
       "      <td>Speed (1994)</td>\n",
       "      <td>Action|Romance|Thriller</td>\n",
       "      <td>1.142113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>1.076539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>357</td>\n",
       "      <td>Four Weddings and a Funeral (1994)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1.065911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>592</td>\n",
       "      <td>Batman (1989)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>1.048861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                      title  \\\n",
       "246      296                        Pulp Fiction (1994)   \n",
       "294      356                        Forrest Gump (1994)   \n",
       "497      608                               Fargo (1996)   \n",
       "487      590                  Dances with Wolves (1990)   \n",
       "316      380                           True Lies (1994)   \n",
       "40        47                Seven (a.k.a. Se7en) (1995)   \n",
       "313      377                               Speed (1994)   \n",
       "214      260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "295      357         Four Weddings and a Funeral (1994)   \n",
       "488      592                              Batman (1989)   \n",
       "\n",
       "                                       genres  Predictions  \n",
       "246               Comedy|Crime|Drama|Thriller     2.060064  \n",
       "294                  Comedy|Drama|Romance|War     1.772814  \n",
       "497               Comedy|Crime|Drama|Thriller     1.526111  \n",
       "487                   Adventure|Drama|Western     1.508960  \n",
       "316  Action|Adventure|Comedy|Romance|Thriller     1.276646  \n",
       "40                           Mystery|Thriller     1.265219  \n",
       "313                   Action|Romance|Thriller     1.142113  \n",
       "214                   Action|Adventure|Sci-Fi     1.076539  \n",
       "295                            Comedy|Romance     1.065911  \n",
       "488                     Action|Crime|Thriller     1.048861  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 추천을 할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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