Exploration/E9_movie_recommendation/E9_movie_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "thrown-python",
   "metadata": {},
   "source": [
    "# 1) 데이터 준비와 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "straight-korea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968\n",
       "3        1      3408       4  978300275\n",
       "4        1      2355       5  978824291"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "rating_file_path=os.getenv('HOME') + '/aiffel/recommendata_iu/data/ml-1m/ratings.dat'\n",
    "ratings_cols = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_csv(rating_file_path, sep='::', names=ratings_cols, engine='python', encoding = \"ISO-8859-1\")\n",
    "orginal_data_size = len(ratings)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "developing-mining",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orginal_data_size: 1000209, filtered_data_size: 836478\n",
      "Ratio of Remaining Data is 83.63%\n"
     ]
    }
   ],
   "source": [
    "# 3점 이상만 남깁니다.\n",
    "ratings = ratings[ratings['rating']>=3]\n",
    "filtered_data_size = len(ratings)\n",
    "\n",
    "print(f'orginal_data_size: {orginal_data_size}, filtered_data_size: {filtered_data_size}')\n",
    "print(f'Ratio of Remaining Data is {filtered_data_size / orginal_data_size:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "comparable-while",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rating 컬럼의 이름을 count로 바꿉니다.\n",
    "ratings.rename(columns={'rating':'count'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "charitable-microwave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</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>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|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</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": [
       "   movie_id                               title                         genre\n",
       "0         1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1         2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 영화 제목을 보기 위해 메타 데이터를 읽어옵니다.\n",
    "movie_file_path=os.getenv('HOME') + '/aiffel/recommendata_iu/data/ml-1m/movies.dat'\n",
    "cols = ['movie_id', 'title', 'genre'] \n",
    "movies = pd.read_csv(movie_file_path, sep='::', names=cols, engine='python', encoding='ISO-8859-1')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ambient-grade",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>count</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>978300275</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836473</th>\n",
       "      <td>6040</td>\n",
       "      <td>1090</td>\n",
       "      <td>3</td>\n",
       "      <td>956715518</td>\n",
       "      <td>Platoon (1986)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836474</th>\n",
       "      <td>6040</td>\n",
       "      <td>1094</td>\n",
       "      <td>5</td>\n",
       "      <td>956704887</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836475</th>\n",
       "      <td>6040</td>\n",
       "      <td>562</td>\n",
       "      <td>5</td>\n",
       "      <td>956704746</td>\n",
       "      <td>Welcome to the Dollhouse (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836476</th>\n",
       "      <td>6040</td>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>956715648</td>\n",
       "      <td>Sophie's Choice (1982)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836477</th>\n",
       "      <td>6040</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>956715569</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>Children's|Drama|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836478 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  movie_id  count  timestamp  \\\n",
       "0             1      1193      5  978300760   \n",
       "1             1       661      3  978302109   \n",
       "2             1       914      3  978301968   \n",
       "3             1      3408      4  978300275   \n",
       "4             1      2355      5  978824291   \n",
       "...         ...       ...    ...        ...   \n",
       "836473     6040      1090      3  956715518   \n",
       "836474     6040      1094      5  956704887   \n",
       "836475     6040       562      5  956704746   \n",
       "836476     6040      1096      4  956715648   \n",
       "836477     6040      1097      4  956715569   \n",
       "\n",
       "                                         title  \\\n",
       "0       One Flew Over the Cuckoo's Nest (1975)   \n",
       "1             James and the Giant Peach (1996)   \n",
       "2                          My Fair Lady (1964)   \n",
       "3                       Erin Brockovich (2000)   \n",
       "4                         Bug's Life, A (1998)   \n",
       "...                                        ...   \n",
       "836473                          Platoon (1986)   \n",
       "836474                 Crying Game, The (1992)   \n",
       "836475         Welcome to the Dollhouse (1995)   \n",
       "836476                  Sophie's Choice (1982)   \n",
       "836477       E.T. the Extra-Terrestrial (1982)   \n",
       "\n",
       "                                  genre  \n",
       "0                                 Drama  \n",
       "1          Animation|Children's|Musical  \n",
       "2                       Musical|Romance  \n",
       "3                                 Drama  \n",
       "4           Animation|Children's|Comedy  \n",
       "...                                 ...  \n",
       "836473                        Drama|War  \n",
       "836474                Drama|Romance|War  \n",
       "836475                     Comedy|Drama  \n",
       "836476                            Drama  \n",
       "836477  Children's|Drama|Fantasy|Sci-Fi  \n",
       "\n",
       "[836478 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings=pd.merge(ratings, movies, on=\"movie_id\", how=\"left\")\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "disabled-delta",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead 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>user_id</th>\n",
       "      <th>title</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>James and the Giant Peach (1996)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>My Fair Lady (1964)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Erin Brockovich (2000)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Ben-Hur (1959)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>Wizard of Oz, The (1939)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                                   title  count\n",
       "0        1  One Flew Over the Cuckoo's Nest (1975)      5\n",
       "1        1        James and the Giant Peach (1996)      3\n",
       "2        1                     My Fair Lady (1964)      3\n",
       "3        1                  Erin Brockovich (2000)      4\n",
       "4        1                    Bug's Life, A (1998)      5\n",
       "5        1              Princess Bride, The (1987)      3\n",
       "6        1                          Ben-Hur (1959)      5\n",
       "7        1               Christmas Story, A (1983)      5\n",
       "8        1  Snow White and the Seven Dwarfs (1937)      4\n",
       "9        1                Wizard of Oz, The (1939)      4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필요없는 정보 삭제\n",
    "using_cols=['user_id','title','count']\n",
    "ratings=ratings[using_cols]\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "inappropriate-allah",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py:4446: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  errors=errors,\n"
     ]
    }
   ],
   "source": [
    "# title 컬럼의 이름을 movie로 바꿉니다.\n",
    "ratings.rename(columns={'title':'movie'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optimum-incident",
   "metadata": {},
   "source": [
    "# 2) 분석해 봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "naked-phone",
   "metadata": {},
   "source": [
    "- ratings에 있는 유니크한 영화 개수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "thorough-browse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3628"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['movie'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "formed-person",
   "metadata": {},
   "source": [
    "ratings에 있는 유니크한 영화의 개수는 3628개이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "private-karaoke",
   "metadata": {},
   "source": [
    "- ratings에 있는 유니크한 사용자 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "essential-tanzania",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6039"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "billion-processing",
   "metadata": {},
   "source": [
    "ratings에 있는 유니크한 사용사의 수는 6040명이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "entitled-shoulder",
   "metadata": {},
   "source": [
    "- 가장 인기 있는 영화 30개(인기순)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "christian-curve",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie\n",
       "American Beauty (1999)                                   3211\n",
       "Star Wars: Episode IV - A New Hope (1977)                2910\n",
       "Star Wars: Episode V - The Empire Strikes Back (1980)    2885\n",
       "Star Wars: Episode VI - Return of the Jedi (1983)        2716\n",
       "Saving Private Ryan (1998)                               2561\n",
       "Terminator 2: Judgment Day (1991)                        2509\n",
       "Silence of the Lambs, The (1991)                         2498\n",
       "Raiders of the Lost Ark (1981)                           2473\n",
       "Back to the Future (1985)                                2460\n",
       "Matrix, The (1999)                                       2434\n",
       "Jurassic Park (1993)                                     2413\n",
       "Sixth Sense, The (1999)                                  2385\n",
       "Fargo (1996)                                             2371\n",
       "Braveheart (1995)                                        2314\n",
       "Men in Black (1997)                                      2297\n",
       "Schindler's List (1993)                                  2257\n",
       "Princess Bride, The (1987)                               2252\n",
       "Shakespeare in Love (1998)                               2213\n",
       "L.A. Confidential (1997)                                 2210\n",
       "Shawshank Redemption, The (1994)                         2194\n",
       "Godfather, The (1972)                                    2167\n",
       "Groundhog Day (1993)                                     2121\n",
       "E.T. the Extra-Terrestrial (1982)                        2102\n",
       "Being John Malkovich (1999)                              2066\n",
       "Ghostbusters (1984)                                      2051\n",
       "Pulp Fiction (1994)                                      2030\n",
       "Forrest Gump (1994)                                      2022\n",
       "Terminator, The (1984)                                   2019\n",
       "Toy Story (1995)                                         2000\n",
       "Fugitive, The (1993)                                     1941\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count=ratings.groupby('movie')['user_id'].count()\n",
    "movie_count.sort_values(ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beginning-evans",
   "metadata": {},
   "source": [
    "# 3) 내가 선호하는 영화를 5가지 골라서 rating에 추가해 줍시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "equipped-graph",
   "metadata": {
    "scrolled": true
   },
   "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>user_id</th>\n",
       "      <th>movie</th>\n",
       "      <th>count</th>\n",
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       "      <th>836473</th>\n",
       "      <td>6040</td>\n",
       "      <td>Platoon (1986)</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>836474</th>\n",
       "      <td>6040</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836475</th>\n",
       "      <td>6040</td>\n",
       "      <td>Welcome to the Dollhouse (1995)</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>836476</th>\n",
       "      <td>6040</td>\n",
       "      <td>Sophie's Choice (1982)</td>\n",
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       "      <th>836477</th>\n",
       "      <td>6040</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yejin</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>yejin</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yejin</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>yejin</td>\n",
       "      <td>Matrix, The (1999)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>yejin</td>\n",
       "      <td>Silence of the Lambs, The (1991)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id                                      movie  count\n",
       "836473    6040                             Platoon (1986)      3\n",
       "836474    6040                    Crying Game, The (1992)      5\n",
       "836475    6040            Welcome to the Dollhouse (1995)      5\n",
       "836476    6040                     Sophie's Choice (1982)      4\n",
       "836477    6040          E.T. the Extra-Terrestrial (1982)      4\n",
       "0        yejin                           Toy Story (1995)      5\n",
       "1        yejin          E.T. the Extra-Terrestrial (1982)      5\n",
       "2        yejin  Star Wars: Episode IV - A New Hope (1977)      5\n",
       "3        yejin                         Matrix, The (1999)      5\n",
       "4        yejin           Silence of the Lambs, The (1991)      5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_favorite = ['Toy Story (1995)', 'E.T. the Extra-Terrestrial (1982)', 'Star Wars: Episode IV - A New Hope (1977)', 'Matrix, The (1999)', 'Silence of the Lambs, The (1991)']\n",
    "\n",
    "my_playlist = pd.DataFrame({'user_id': ['yejin']*5, 'movie': my_favorite, 'count':[5]*5})\n",
    "\n",
    "if not ratings.isin({'user_id':['yejin']})['user_id'].any():  # user_id에 'yejin'이라는 데이터가 없다면\n",
    "    ratings = ratings.append(my_playlist)                           # 위에 임의로 만든 my_favorite 데이터를 추가해 줍니다. \n",
    "\n",
    "ratings.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "welsh-artwork",
   "metadata": {},
   "source": [
    "# 4) CSR matrix를 직접 만들어 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "terminal-batch",
   "metadata": {},
   "outputs": [],
   "source": [
    "#고유한 유저, 아티스트를 찾아내는 코드\n",
    "user_unique=ratings['user_id'].unique()\n",
    "movie_unique=ratings['movie'].unique()\n",
    "\n",
    "#유저, 아티스트 indexing 하는 코드 idx는 index의 약자\n",
    "user_to_idx={v:k for k,v in enumerate(user_unique)}\n",
    "movie_to_idx={v:k for k,v in enumerate(movie_unique)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "funded-mason",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6039\n",
      "1030\n"
     ]
    }
   ],
   "source": [
    "# 인덱싱이 잘 되었는지 확인해 봅니다. \n",
    "print(user_to_idx['yejin']) \n",
    "print(movie_to_idx['Platoon (1986)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bronze-wayne",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id column indexing OK!!\n",
      "movie column indexing OK!!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>user_id</th>\n",
       "      <th>movie</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6039</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6039</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6039</td>\n",
       "      <td>44</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6039</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6039</td>\n",
       "      <td>121</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836483 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  movie  count\n",
       "0         0      0      5\n",
       "1         0      1      3\n",
       "2         0      2      3\n",
       "3         0      3      4\n",
       "4         0      4      5\n",
       "..      ...    ...    ...\n",
       "0      6039     40      5\n",
       "1      6039     26      5\n",
       "2      6039     44      5\n",
       "3      6039    124      5\n",
       "4      6039    121      5\n",
       "\n",
       "[836483 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_user_data = ratings['user_id'].map(user_to_idx.get).dropna()\n",
    "if len(temp_user_data) == len(ratings):   # 모든 row가 정상적으로 인덱싱되었다면\n",
    "    print('user_id column indexing OK!!')\n",
    "    ratings['user_id'] = temp_user_data   # data['user_id']을 인덱싱된 Series로 교체해 줍니다. \n",
    "else:\n",
    "    print('user_id column indexing Fail!!')\n",
    "\n",
    "# movie_to_idx을 통해 movie_id 컬럼도 동일한 방식으로 인덱싱해 줍니다. \n",
    "temp_movie_data = ratings['movie'].map(movie_to_idx.get).dropna()\n",
    "if len(temp_movie_data) == len(ratings):\n",
    "    print('movie column indexing OK!!')\n",
    "    ratings['movie'] = temp_movie_data\n",
    "else:\n",
    "    print('movie column indexing Fail!!')\n",
    "\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "light-advantage",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<6040x3628 sparse matrix of type '<class 'numpy.longlong'>'\n",
       "\twith 836483 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "num_user = ratings['user_id'].nunique()\n",
    "num_movie = ratings['movie'].nunique()\n",
    "\n",
    "csr_data = csr_matrix((ratings['count'], (ratings['user_id'], ratings['movie'])), shape=(num_user, num_movie) )\n",
    "csr_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "substantial-firmware",
   "metadata": {},
   "source": [
    "# 5) als_model = AlternatingLeastSquares 모델을 직접 구성하여 훈련시켜 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "korean-influence",
   "metadata": {},
   "outputs": [],
   "source": [
    "from implicit.als import AlternatingLeastSquares\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# implicit 라이브러리에서 권장하고 있는 부분입니다. 학습 내용과는 무관합니다.\n",
    "os.environ['OPENBLAS_NUM_THREADS']='1'\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "os.environ['MKL_NUM_THREADS']='1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "potential-luther",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implicit AlternatingLeastSquares 모델의 선언\n",
    "als_model = AlternatingLeastSquares(factors=100, regularization=0.01, use_gpu=False, iterations=10, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "consecutive-policy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3628x6040 sparse matrix of type '<class 'numpy.longlong'>'\n",
       "\twith 836483 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# als 모델은 input으로 (item X user 꼴의 matrix를 받기 때문에 Transpose해줍니다.)\n",
    "csr_data_transpose=csr_data.T\n",
    "csr_data_transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dirty-mortality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "718e8e6a8e7c4349bf3d21d3173caf40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "als_model.fit(csr_data_transpose)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cross-absolute",
   "metadata": {},
   "source": [
    "# 6) 내가 선호하는 5가지 영화 중 하나와 그 외의 영화 하나를 골라 훈련된 모델이 예측한 나의 선호도를 파악해 보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "requested-subdivision",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델이 예측한 나의 선호도\n",
    "# 1. 내가 선호하는 영화인 Toy Story (1995)에 대한 선호도\n",
    "yejin, Toy_Story = user_to_idx['yejin'], movie_to_idx['Toy Story (1995)']\n",
    "yejin_vector, Toy_Story_vector = als_model.user_factors[yejin], als_model.item_factors[Toy_Story]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "assigned-metallic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49298796"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(yejin_vector, Toy_Story_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "convinced-reproduction",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2. 내가 선호하지 않는 영화인 Ghostbusters (1984)에 대한 선호도\n",
    "Ghostbusters = movie_to_idx['Ghostbusters (1984)']\n",
    "Ghostbusters_vector = als_model.item_factors[Ghostbusters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "gorgeous-annual",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13597515"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(yejin_vector, Ghostbusters_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "generous-billion",
   "metadata": {},
   "source": [
    "확실히 선호하는 영화에 대한 값은 0.49298796로 선호하지 않은 영화에 대한 값인 0.13597515 보다 훨씬 컸다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stuffed-singing",
   "metadata": {},
   "source": [
    "# 7) 내가 좋아하는 영화와 비슷한 영화를 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "excited-burton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Toy Story (1995)',\n",
       " 'Toy Story 2 (1999)',\n",
       " 'Babe (1995)',\n",
       " \"Bug's Life, A (1998)\",\n",
       " 'Aladdin (1992)',\n",
       " 'Groundhog Day (1993)',\n",
       " 'Lion King, The (1994)',\n",
       " 'Beauty and the Beast (1991)',\n",
       " 'Pleasantville (1998)',\n",
       " \"There's Something About Mary (1998)\"]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#movie_to_idx 를 뒤집어, index로부터 movie 이름을 얻는 dict를 생성합니다. \n",
    "idx_to_movie = {v:k for k,v in movie_to_idx.items()}\n",
    "\n",
    "def get_similar_movie(movie_title: str):\n",
    "    movie_id = movie_to_idx[movie_title]\n",
    "    similar_movie = als_model.similar_items(movie_id)\n",
    "    similar_movie = [idx_to_movie[i[0]] for i in similar_movie]\n",
    "    return similar_movie\n",
    "\n",
    "get_similar_movie('Toy Story (1995)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "derived-personality",
   "metadata": {},
   "source": [
    "# 8) 내가 가장 좋아할 만한 영화들을 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "regional-first",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Star Wars: Episode V - The Empire Strikes Back (1980)',\n",
       " 'Star Wars: Episode VI - Return of the Jedi (1983)',\n",
       " 'Terminator 2: Judgment Day (1991)',\n",
       " 'Back to the Future (1985)',\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'Babe (1995)',\n",
       " 'Saving Private Ryan (1998)',\n",
       " 'Star Wars: Episode I - The Phantom Menace (1999)',\n",
       " 'Sixth Sense, The (1999)',\n",
       " \"Schindler's List (1993)\",\n",
       " 'Forrest Gump (1994)',\n",
       " 'Toy Story 2 (1999)',\n",
       " 'Fargo (1996)',\n",
       " 'Raiders of the Lost Ark (1981)',\n",
       " 'Terminator, The (1984)',\n",
       " 'American Beauty (1999)',\n",
       " 'Fugitive, The (1993)',\n",
       " 'Close Encounters of the Third Kind (1977)',\n",
       " '2001: A Space Odyssey (1968)',\n",
       " 'Groundhog Day (1993)']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = user_to_idx['yejin']\n",
    "# recommend에서는 user*item CSR Matrix를 받습니다.\n",
    "movie_recommended = als_model.recommend(user, csr_data, N=20, filter_already_liked_items=True)\n",
    "[idx_to_movie[i[0]] for i in movie_recommended]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "endless-appliance",
   "metadata": {},
   "source": [
    "# 회고\n",
    "- 처음에는 ratings과 movies를 합칠 생각을 못했다. csr_matrix를 할 때 movie의 인덱스보다 movie 제목을 바로 넣으면 좋을 것 같다는 생각이 들었고, 시도해봤다.\n",
    "- len() of unsized object 에러가 나와서 한참 헤메다가 ratings.count를 ratings[count]로 수정하니깐 에러가 해결되었다.아직 이유는 잘 모르겠다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specialized-locator",
   "metadata": {},
   "source": [
    "- Matrix Factorization(MF)와 CSR(Compressed Sparse Row)Matrix가 잘 이해가 가지 않아서 몇시간 동안 찾아봤지만 아직도 이해가 완전히 가지 않는다. 나중에 다시 공부해야될것 같다.\n",
    "- 추천시스템을 이용만 해보다가 직접 간단하게 구현을 해보니깐 신기했다."
   ]
  }
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