EXPLORATION/Node_14/[E-14] Movie_Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 14. 아이유팬이 좋아할 만한 다른 아티스트 찾기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14-9. 프로젝트 - Movielens 영화 추천 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 목차\n",
    "- 1) 데이터 준비와 전처리\n",
    "    - 추천 시스템의 MNIST 라고 부를만한 Movielens 데이터 사용\n",
    "        - 유저가 영화에 대해 평점을 매긴 데이터: [Movielens 1M Dataset](https://grouplens.org/datasets/movielens/1m/)\n",
    "        - 데이터 설명: [MovieLens 1M Dataset README.txt](https://files.grouplens.org/datasets/movielens/ml-1m-README.txt)\n",
    "    - 별점 데이터: explicit 데이터\n",
    "        - implicit 데이터로 간주하고 테스트해 볼 수 있음\n",
    "    - 별점 == 시청횟수\n",
    "    - 유저가 3점 미만으로 준 데이터는 선호하지 않는다고 가정하고 제외\n",
    "- 2) 분석해 봅시다.\n",
    "- 3) 내가 선호하는 영화를 5가지 골라서 ratings에 추가해 줍시다.\n",
    "- 4) CSR matrix를 직접 만들어 봅시다.\n",
    "- 5) als_model = AlternatingLeastSquares 모델을 직접 구성하여 훈련시켜 봅시다.\n",
    "- 6) 내가 선호하는 5가지 영화 중 하나와 그 외의 영화 하나를 골라 훈련된 모델이 예측한 나의 선호도를 파악해 보세요.\n",
    "- 7) 내가 좋아하는 영화와 비슷한 영화를 추천받아 봅시다.\n",
    "- 8) 내가 가장 좋아할 만한 영화들을 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) 데이터 준비와 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 기본 데이터(평점) 불러오기: Movielens 데이터 `rating.dat` (UserID::MovieID::Rating::Timestamp)\n",
    "- UserIDs range between 1 and 6040 \n",
    "- MovieIDs range between 1 and 3952\n",
    "- Ratings are made on a 5-star scale (whole-star ratings only)\n",
    "- Timestamp is represented in seconds since the epoch as returned by time(2)\n",
    "- Each user has at least 20 ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>ratings</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000204</th>\n",
       "      <td>6040</td>\n",
       "      <td>1091</td>\n",
       "      <td>1</td>\n",
       "      <td>956716541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>6040</td>\n",
       "      <td>1094</td>\n",
       "      <td>5</td>\n",
       "      <td>956704887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>6040</td>\n",
       "      <td>562</td>\n",
       "      <td>5</td>\n",
       "      <td>956704746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>6040</td>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>956715648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>6040</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>956715569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000209 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  movie_id  ratings  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",
       "1000204     6040      1091        1  956716541\n",
       "1000205     6040      1094        5  956704887\n",
       "1000206     6040       562        5  956704746\n",
       "1000207     6040      1096        4  956715648\n",
       "1000208     6040      1097        4  956715569\n",
       "\n",
       "[1000209 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_file_path='recommendata_iu/data/ml-1m/ratings.dat'\n",
    "ratings_cols = ['user_id', 'movie_id', 'ratings', '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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 원본 기본 데이터 보관"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000209\n"
     ]
    }
   ],
   "source": [
    "ratings_orig = ratings.copy()\n",
    "ratings_orig_size = len(ratings)\n",
    "print(ratings_orig_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 유저별 컬럼에 대한 통계 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6040.000000\n",
       "mean      165.597517\n",
       "std       192.747029\n",
       "min        20.000000\n",
       "25%        44.000000\n",
       "50%        96.000000\n",
       "75%       208.000000\n",
       "max      2314.000000\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 유저별 몇 개의 영화를 보고 있는지에 대한 통계\n",
    "user_count_orig = ratings_orig.groupby('user_id')['movie_id'].count()\n",
    "user_count_orig.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6040.000000\n",
       "mean        3.840811\n",
       "std         0.577449\n",
       "min         1.000000\n",
       "25%         4.000000\n",
       "50%         4.000000\n",
       "75%         4.000000\n",
       "max         5.000000\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 유저별 시청횟수 중앙값에 대한 통계\n",
    "user_median_orig = ratings_orig.groupby('user_id')['ratings'].median()\n",
    "user_median_orig.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 별점 개수(ratings)와 사용자 수(users) 관계 막대그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings_users = ratings.groupby(by=['ratings']).agg({'user_id': 'count'}).reset_index()\n",
    "ratings_users.columns = ['ratings', 'users']\n",
    "ratings_users_dic = {ratings:users for ratings, users in zip(ratings_users.ratings, ratings_users.users)}\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.barh(ratings_users.ratings, ratings_users.users, align='center', alpha=0.3, color='g')\n",
    "plt.yticks(ratings_users.ratings)\n",
    "plt.xticks(ratings_users.users)\n",
    "plt.legend(ratings_users.columns)\n",
    "plt.ylabel('ratings')\n",
    "plt.xlabel('users')\n",
    "plt.grid(ls='dotted')\n",
    "plt.title('The relationship between Ratings and Users')\n",
    "\n",
    "for i, users in enumerate(ratings_users.users):\n",
    "    if i >= 2:\n",
    "        color = 'r'\n",
    "    else:\n",
    "        color = 'b'\n",
    "    plt.text(users+i,1+i,users, horizontalalignment='center', verticalalignment='center', color=color)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 유저가 3점 미만으로 준 데이터는 선호하지 않는다고 가정하고 제외"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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['ratings']>=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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\Anaconda3\\envs\\aiffel_3.8\\lib\\site-packages\\pandas\\core\\frame.py:4296: 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",
      "  return super().rename(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0          5\n",
       "1          3\n",
       "2          3\n",
       "3          4\n",
       "4          5\n",
       "          ..\n",
       "1000203    3\n",
       "1000205    5\n",
       "1000206    5\n",
       "1000207    4\n",
       "1000208    4\n",
       "Name: counts, Length: 836478, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ratings 컬럼의 이름을 counts로 바꿉니다.\n",
    "ratings.rename(columns={'ratings':'counts'}, inplace=True)\n",
    "ratings['counts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 유저별 컬럼에 대한 통계 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6039.000000\n",
       "mean      138.512668\n",
       "std       156.241599\n",
       "min         1.000000\n",
       "25%        38.000000\n",
       "50%        81.000000\n",
       "75%       177.000000\n",
       "max      1968.000000\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 유저별 몇 개의 영화를 보고 있는지에 대한 통계\n",
    "user_count = ratings.groupby('user_id')['movie_id'].count()\n",
    "user_count.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6039.000000\n",
       "mean        4.055970\n",
       "std         0.432143\n",
       "min         3.000000\n",
       "25%         4.000000\n",
       "50%         4.000000\n",
       "75%         4.000000\n",
       "max         5.000000\n",
       "Name: counts, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 유저별 시청횟수 중앙값에 대한 통계\n",
    "user_median = ratings.groupby('user_id')['counts'].median()\n",
    "user_median.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Convert timestamp to datetime with pandas [[참고]](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html)\n",
    "    - pd.to_datetime(arg, unit='s') => year-month-day hh:mm:ss\n",
    "        - arg: int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like\n",
    "        - unit: str, default ‘ns’ (D,s,ms,us,ns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_28036\\2721094061.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')\n"
     ]
    },
    {
     "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</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>2000-12-31 22:12:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:35:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:32:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:04:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000203</th>\n",
       "      <td>6040</td>\n",
       "      <td>1090</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-04-26 02:18:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>6040</td>\n",
       "      <td>1094</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:21:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>6040</td>\n",
       "      <td>562</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:19:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>6040</td>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:20:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>6040</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:19:29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>836478 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  movie_id  counts           timestamp\n",
       "0              1      1193       5 2000-12-31 22:12:40\n",
       "1              1       661       3 2000-12-31 22:35:09\n",
       "2              1       914       3 2000-12-31 22:32:48\n",
       "3              1      3408       4 2000-12-31 22:04:35\n",
       "4              1      2355       5 2001-01-06 23:38:11\n",
       "...          ...       ...     ...                 ...\n",
       "1000203     6040      1090       3 2000-04-26 02:18:38\n",
       "1000205     6040      1094       5 2000-04-25 23:21:27\n",
       "1000206     6040       562       5 2000-04-25 23:19:06\n",
       "1000207     6040      1096       4 2000-04-26 02:20:48\n",
       "1000208     6040      1097       4 2000-04-26 02:19:29\n",
       "\n",
       "[836478 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['timestamp'] = pd.to_datetime(ratings['timestamp'], unit='s')\n",
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0\n",
       "movie_id     0\n",
       "counts       0\n",
       "timestamp    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치 확인\n",
    "ratings.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 836478 entries, 0 to 1000208\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype         \n",
      "---  ------     --------------   -----         \n",
      " 0   user_id    836478 non-null  int64         \n",
      " 1   movie_id   836478 non-null  int64         \n",
      " 2   counts     836478 non-null  int64         \n",
      " 3   timestamp  836478 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(3)\n",
      "memory usage: 31.9 MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 첫 번째 유저가 어떤 영화를 봤는지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_1 이 시청한 영화 개수: 53\n"
     ]
    },
    {
     "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</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>2000-12-31 22:12:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:35:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:32:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3408</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:04:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1197</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1287</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:33:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:11:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>919</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:22:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>595</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>938</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2398</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:38:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2918</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:35:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1035</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>2791</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:36:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>2687</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3105</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:28:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>2797</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:33:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>2321</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:36:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:12:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>527</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:36:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:39:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:32:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1545</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:35:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>745</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>2294</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1566</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>1907</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>783</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>1836</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:02:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>2762</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:34:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:26:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>1962</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>2692</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:26:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:12:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>1028</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:36:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>1207</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:11:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>2028</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:26:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:35:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>3114</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:36:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>608</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:23:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:34:51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  movie_id  counts           timestamp\n",
       "0         1      1193       5 2000-12-31 22:12:40\n",
       "1         1       661       3 2000-12-31 22:35:09\n",
       "2         1       914       3 2000-12-31 22:32:48\n",
       "3         1      3408       4 2000-12-31 22:04:35\n",
       "4         1      2355       5 2001-01-06 23:38:11\n",
       "5         1      1197       3 2000-12-31 22:37:48\n",
       "6         1      1287       5 2000-12-31 22:33:59\n",
       "7         1      2804       5 2000-12-31 22:11:59\n",
       "8         1       594       4 2000-12-31 22:37:48\n",
       "9         1       919       4 2000-12-31 22:22:48\n",
       "10        1       595       5 2001-01-06 23:37:48\n",
       "11        1       938       4 2000-12-31 22:29:12\n",
       "12        1      2398       4 2000-12-31 22:38:01\n",
       "13        1      2918       4 2000-12-31 22:35:24\n",
       "14        1      1035       5 2000-12-31 22:29:13\n",
       "15        1      2791       4 2000-12-31 22:36:28\n",
       "16        1      2687       3 2001-01-06 23:37:48\n",
       "17        1      2018       4 2000-12-31 22:29:37\n",
       "18        1      3105       5 2000-12-31 22:28:33\n",
       "19        1      2797       4 2000-12-31 22:33:59\n",
       "20        1      2321       3 2000-12-31 22:36:45\n",
       "21        1       720       3 2000-12-31 22:12:40\n",
       "22        1      1270       5 2000-12-31 22:00:55\n",
       "23        1       527       5 2001-01-06 23:36:35\n",
       "24        1      2340       3 2000-12-31 22:01:43\n",
       "25        1        48       5 2001-01-06 23:39:11\n",
       "26        1      1097       4 2000-12-31 22:32:33\n",
       "27        1      1721       4 2000-12-31 22:00:55\n",
       "28        1      1545       4 2001-01-06 23:35:39\n",
       "29        1       745       3 2001-01-06 23:37:48\n",
       "30        1      2294       4 2001-01-06 23:38:11\n",
       "31        1      3186       4 2000-12-31 22:00:19\n",
       "32        1      1566       4 2001-01-06 23:38:50\n",
       "33        1       588       4 2001-01-06 23:37:48\n",
       "34        1      1907       4 2001-01-06 23:38:50\n",
       "35        1       783       4 2001-01-06 23:38:11\n",
       "36        1      1836       5 2000-12-31 22:02:52\n",
       "37        1      1022       5 2000-12-31 22:00:55\n",
       "38        1      2762       4 2000-12-31 22:34:51\n",
       "39        1       150       5 2000-12-31 22:29:37\n",
       "40        1         1       5 2001-01-06 23:37:48\n",
       "41        1      1961       5 2000-12-31 22:26:30\n",
       "42        1      1962       4 2000-12-31 22:29:13\n",
       "43        1      2692       4 2000-12-31 22:26:10\n",
       "44        1       260       4 2000-12-31 22:12:40\n",
       "45        1      1028       5 2000-12-31 22:29:37\n",
       "46        1      1029       5 2000-12-31 22:36:45\n",
       "47        1      1207       4 2000-12-31 22:11:59\n",
       "48        1      2028       5 2000-12-31 22:26:59\n",
       "49        1       531       4 2000-12-31 22:35:49\n",
       "50        1      3114       4 2000-12-31 22:36:14\n",
       "51        1       608       4 2000-12-31 22:23:18\n",
       "52        1      1246       4 2000-12-31 22:34:51"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_user_1 = (ratings['user_id']==ratings.loc[0, 'user_id'])\n",
    "movies_user_1 = ratings.loc[ratings_user_1]\n",
    "print(f'user_1 이 시청한 영화 개수: {len(movies_user_1)}')\n",
    "movies_user_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 메타 데이터(영화) 불러오기: Movielens 데이터 `movies.dat` (MovieID::Title::Genres)\n",
    "- Titles are identical to titles provided by the IMDB (including\n",
    "year of release)\n",
    "- Genres: Action, Adventure, Animation, Children's, Comedy, Crime, Documentary, Drama, Fantasy, Film-Noir, Horror, Musical, Mystery, Romance, Sci-Fi, Thriller, War, Western\n",
    "- Some MovieIDs do not correspond to a movie due to accidental duplicate\n",
    "entries and/or test entries\n",
    "- Movies are mostly entered by hand, so errors and inconsistencies may exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3948</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                               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",
       "3878      3948             Meet the Parents (2000)   \n",
       "3879      3949          Requiem for a Dream (2000)   \n",
       "3880      3950                    Tigerland (2000)   \n",
       "3881      3951             Two Family House (2000)   \n",
       "3882      3952               Contender, The (2000)   \n",
       "\n",
       "                             genre  \n",
       "0      Animation|Children's|Comedy  \n",
       "1     Adventure|Children's|Fantasy  \n",
       "2                   Comedy|Romance  \n",
       "3                     Comedy|Drama  \n",
       "4                           Comedy  \n",
       "...                            ...  \n",
       "3878                        Comedy  \n",
       "3879                         Drama  \n",
       "3880                         Drama  \n",
       "3881                         Drama  \n",
       "3882                Drama|Thriller  \n",
       "\n",
       "[3883 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 영화 제목을 보기 위해 메타 데이터를 읽어옵니다.\n",
    "movie_file_path='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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 원본 메타 데이터 보관"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3883\n"
     ]
    }
   ],
   "source": [
    "movies_orig = movies.copy()\n",
    "movies_orig_size = len(movies)\n",
    "print(movies_orig_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id    0\n",
       "title       0\n",
       "genre       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치 확인\n",
    "movies.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3883 entries, 0 to 3882\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   movie_id  3883 non-null   int64 \n",
      " 1   title     3883 non-null   object\n",
      " 2   genre     3883 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 91.1+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 데이터 합치기 [[참고1]](https://yganalyst.github.io/data_handling/Pd_12/) [[참고2]](https://www.delftstack.com/ko/howto/python-pandas/pandas-groupby-count-python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</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>2000-12-31 22:12:40</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>2000-12-31 22:35:09</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>2000-12-31 22:32:48</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>2000-12-31 22:04:35</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>2001-01-06 23:38:11</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1197</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1287</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:33:59</td>\n",
       "      <td>Ben-Hur (1959)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:11:59</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>919</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:22:48</td>\n",
       "      <td>Wizard of Oz, The (1939)</td>\n",
       "      <td>Adventure|Children's|Drama|Musical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  counts           timestamp  \\\n",
       "0        1      1193       5 2000-12-31 22:12:40   \n",
       "1        1       661       3 2000-12-31 22:35:09   \n",
       "2        1       914       3 2000-12-31 22:32:48   \n",
       "3        1      3408       4 2000-12-31 22:04:35   \n",
       "4        1      2355       5 2001-01-06 23:38:11   \n",
       "5        1      1197       3 2000-12-31 22:37:48   \n",
       "6        1      1287       5 2000-12-31 22:33:59   \n",
       "7        1      2804       5 2000-12-31 22:11:59   \n",
       "8        1       594       4 2000-12-31 22:37:48   \n",
       "9        1       919       4 2000-12-31 22:22:48   \n",
       "\n",
       "                                    title                               genre  \n",
       "0  One Flew Over the Cuckoo's Nest (1975)                               Drama  \n",
       "1        James and the Giant Peach (1996)        Animation|Children's|Musical  \n",
       "2                     My Fair Lady (1964)                     Musical|Romance  \n",
       "3                  Erin Brockovich (2000)                               Drama  \n",
       "4                    Bug's Life, A (1998)         Animation|Children's|Comedy  \n",
       "5              Princess Bride, The (1987)     Action|Adventure|Comedy|Romance  \n",
       "6                          Ben-Hur (1959)              Action|Adventure|Drama  \n",
       "7               Christmas Story, A (1983)                        Comedy|Drama  \n",
       "8  Snow White and the Seven Dwarfs (1937)        Animation|Children's|Musical  \n",
       "9                Wizard of Oz, The (1939)  Adventure|Children's|Drama|Musical  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ratings 와 movies 데이터 프레임 합치기\n",
    "ratings = ratings.join(movies.set_index('movie_id'), on='movie_id')\n",
    "ratings.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 첫 번째 유저(user_id == 1)가 시청한 영화 정보 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_1 이 시청한 영화 개수: 53\n"
     ]
    },
    {
     "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</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>2000-12-31 22:12:40</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>2000-12-31 22:35:09</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>2000-12-31 22:32:48</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>2000-12-31 22:04:35</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>2001-01-06 23:38:11</td>\n",
       "      <td>Bug's Life, A (1998)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1197</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "      <td>Princess Bride, The (1987)</td>\n",
       "      <td>Action|Adventure|Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1287</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:33:59</td>\n",
       "      <td>Ben-Hur (1959)</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2804</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:11:59</td>\n",
       "      <td>Christmas Story, A (1983)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>594</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:37:48</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>919</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:22:48</td>\n",
       "      <td>Wizard of Oz, The (1939)</td>\n",
       "      <td>Adventure|Children's|Drama|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>595</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>938</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:12</td>\n",
       "      <td>Gigi (1958)</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>2398</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:38:01</td>\n",
       "      <td>Miracle on 34th Street (1947)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2918</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:35:24</td>\n",
       "      <td>Ferris Bueller's Day Off (1986)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>1035</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:13</td>\n",
       "      <td>Sound of Music, The (1965)</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>2791</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:36:28</td>\n",
       "      <td>Airplane! (1980)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>2687</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "      <td>Tarzan (1999)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "      <td>Bambi (1942)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3105</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:28:33</td>\n",
       "      <td>Awakenings (1990)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>2797</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:33:59</td>\n",
       "      <td>Big (1988)</td>\n",
       "      <td>Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>2321</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:36:45</td>\n",
       "      <td>Pleasantville (1998)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>720</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:12:40</td>\n",
       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>Back to the Future (1985)</td>\n",
       "      <td>Comedy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>527</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:36:35</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>2340</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-12-31 22:01:43</td>\n",
       "      <td>Meet Joe Black (1998)</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:39:11</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:32:33</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>Children's|Drama|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1721</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>Titanic (1997)</td>\n",
       "      <td>Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1545</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:35:39</td>\n",
       "      <td>Ponette (1996)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>745</td>\n",
       "      <td>3</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "      <td>Close Shave, A (1995)</td>\n",
       "      <td>Animation|Comedy|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>2294</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "      <td>Antz (1998)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>3186</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:00:19</td>\n",
       "      <td>Girl, Interrupted (1999)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>1566</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:50</td>\n",
       "      <td>Hercules (1997)</td>\n",
       "      <td>Adventure|Animation|Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>Animation|Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>1907</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:50</td>\n",
       "      <td>Mulan (1998)</td>\n",
       "      <td>Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>783</td>\n",
       "      <td>4</td>\n",
       "      <td>2001-01-06 23:38:11</td>\n",
       "      <td>Hunchback of Notre Dame, The (1996)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>1836</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:02:52</td>\n",
       "      <td>Last Days of Disco, The (1998)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1</td>\n",
       "      <td>1022</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:00:55</td>\n",
       "      <td>Cinderella (1950)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1</td>\n",
       "      <td>2762</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:34:51</td>\n",
       "      <td>Sixth Sense, The (1999)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2001-01-06 23:37:48</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:26:30</td>\n",
       "      <td>Rain Man (1988)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1</td>\n",
       "      <td>1962</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:29:13</td>\n",
       "      <td>Driving Miss Daisy (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "      <td>2692</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:26:10</td>\n",
       "      <td>Run Lola Run (Lola rennt) (1998)</td>\n",
       "      <td>Action|Crime|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:12:40</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1</td>\n",
       "      <td>1028</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:29:37</td>\n",
       "      <td>Mary Poppins (1964)</td>\n",
       "      <td>Children's|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:36:45</td>\n",
       "      <td>Dumbo (1941)</td>\n",
       "      <td>Animation|Children's|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "      <td>1207</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:11:59</td>\n",
       "      <td>To Kill a Mockingbird (1962)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1</td>\n",
       "      <td>2028</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-12-31 22:26:59</td>\n",
       "      <td>Saving Private Ryan (1998)</td>\n",
       "      <td>Action|Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:35:49</td>\n",
       "      <td>Secret Garden, The (1993)</td>\n",
       "      <td>Children's|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1</td>\n",
       "      <td>3114</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:36:14</td>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>608</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:23:18</td>\n",
       "      <td>Fargo (1996)</td>\n",
       "      <td>Crime|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1</td>\n",
       "      <td>1246</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-12-31 22:34:51</td>\n",
       "      <td>Dead Poets Society (1989)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  movie_id  counts           timestamp  \\\n",
       "0         1      1193       5 2000-12-31 22:12:40   \n",
       "1         1       661       3 2000-12-31 22:35:09   \n",
       "2         1       914       3 2000-12-31 22:32:48   \n",
       "3         1      3408       4 2000-12-31 22:04:35   \n",
       "4         1      2355       5 2001-01-06 23:38:11   \n",
       "5         1      1197       3 2000-12-31 22:37:48   \n",
       "6         1      1287       5 2000-12-31 22:33:59   \n",
       "7         1      2804       5 2000-12-31 22:11:59   \n",
       "8         1       594       4 2000-12-31 22:37:48   \n",
       "9         1       919       4 2000-12-31 22:22:48   \n",
       "10        1       595       5 2001-01-06 23:37:48   \n",
       "11        1       938       4 2000-12-31 22:29:12   \n",
       "12        1      2398       4 2000-12-31 22:38:01   \n",
       "13        1      2918       4 2000-12-31 22:35:24   \n",
       "14        1      1035       5 2000-12-31 22:29:13   \n",
       "15        1      2791       4 2000-12-31 22:36:28   \n",
       "16        1      2687       3 2001-01-06 23:37:48   \n",
       "17        1      2018       4 2000-12-31 22:29:37   \n",
       "18        1      3105       5 2000-12-31 22:28:33   \n",
       "19        1      2797       4 2000-12-31 22:33:59   \n",
       "20        1      2321       3 2000-12-31 22:36:45   \n",
       "21        1       720       3 2000-12-31 22:12:40   \n",
       "22        1      1270       5 2000-12-31 22:00:55   \n",
       "23        1       527       5 2001-01-06 23:36:35   \n",
       "24        1      2340       3 2000-12-31 22:01:43   \n",
       "25        1        48       5 2001-01-06 23:39:11   \n",
       "26        1      1097       4 2000-12-31 22:32:33   \n",
       "27        1      1721       4 2000-12-31 22:00:55   \n",
       "28        1      1545       4 2001-01-06 23:35:39   \n",
       "29        1       745       3 2001-01-06 23:37:48   \n",
       "30        1      2294       4 2001-01-06 23:38:11   \n",
       "31        1      3186       4 2000-12-31 22:00:19   \n",
       "32        1      1566       4 2001-01-06 23:38:50   \n",
       "33        1       588       4 2001-01-06 23:37:48   \n",
       "34        1      1907       4 2001-01-06 23:38:50   \n",
       "35        1       783       4 2001-01-06 23:38:11   \n",
       "36        1      1836       5 2000-12-31 22:02:52   \n",
       "37        1      1022       5 2000-12-31 22:00:55   \n",
       "38        1      2762       4 2000-12-31 22:34:51   \n",
       "39        1       150       5 2000-12-31 22:29:37   \n",
       "40        1         1       5 2001-01-06 23:37:48   \n",
       "41        1      1961       5 2000-12-31 22:26:30   \n",
       "42        1      1962       4 2000-12-31 22:29:13   \n",
       "43        1      2692       4 2000-12-31 22:26:10   \n",
       "44        1       260       4 2000-12-31 22:12:40   \n",
       "45        1      1028       5 2000-12-31 22:29:37   \n",
       "46        1      1029       5 2000-12-31 22:36:45   \n",
       "47        1      1207       4 2000-12-31 22:11:59   \n",
       "48        1      2028       5 2000-12-31 22:26:59   \n",
       "49        1       531       4 2000-12-31 22:35:49   \n",
       "50        1      3114       4 2000-12-31 22:36:14   \n",
       "51        1       608       4 2000-12-31 22:23:18   \n",
       "52        1      1246       4 2000-12-31 22:34:51   \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",
       "5                          Princess Bride, The (1987)   \n",
       "6                                      Ben-Hur (1959)   \n",
       "7                           Christmas Story, A (1983)   \n",
       "8              Snow White and the Seven Dwarfs (1937)   \n",
       "9                            Wizard of Oz, The (1939)   \n",
       "10                        Beauty and the Beast (1991)   \n",
       "11                                        Gigi (1958)   \n",
       "12                      Miracle on 34th Street (1947)   \n",
       "13                    Ferris Bueller's Day Off (1986)   \n",
       "14                         Sound of Music, The (1965)   \n",
       "15                                   Airplane! (1980)   \n",
       "16                                      Tarzan (1999)   \n",
       "17                                       Bambi (1942)   \n",
       "18                                  Awakenings (1990)   \n",
       "19                                         Big (1988)   \n",
       "20                               Pleasantville (1998)   \n",
       "21  Wallace & Gromit: The Best of Aardman Animatio...   \n",
       "22                          Back to the Future (1985)   \n",
       "23                            Schindler's List (1993)   \n",
       "24                              Meet Joe Black (1998)   \n",
       "25                                  Pocahontas (1995)   \n",
       "26                  E.T. the Extra-Terrestrial (1982)   \n",
       "27                                     Titanic (1997)   \n",
       "28                                     Ponette (1996)   \n",
       "29                              Close Shave, A (1995)   \n",
       "30                                        Antz (1998)   \n",
       "31                           Girl, Interrupted (1999)   \n",
       "32                                    Hercules (1997)   \n",
       "33                                     Aladdin (1992)   \n",
       "34                                       Mulan (1998)   \n",
       "35                Hunchback of Notre Dame, The (1996)   \n",
       "36                     Last Days of Disco, The (1998)   \n",
       "37                                  Cinderella (1950)   \n",
       "38                            Sixth Sense, The (1999)   \n",
       "39                                   Apollo 13 (1995)   \n",
       "40                                   Toy Story (1995)   \n",
       "41                                    Rain Man (1988)   \n",
       "42                          Driving Miss Daisy (1989)   \n",
       "43                   Run Lola Run (Lola rennt) (1998)   \n",
       "44          Star Wars: Episode IV - A New Hope (1977)   \n",
       "45                                Mary Poppins (1964)   \n",
       "46                                       Dumbo (1941)   \n",
       "47                       To Kill a Mockingbird (1962)   \n",
       "48                         Saving Private Ryan (1998)   \n",
       "49                          Secret Garden, The (1993)   \n",
       "50                                 Toy Story 2 (1999)   \n",
       "51                                       Fargo (1996)   \n",
       "52                          Dead Poets Society (1989)   \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",
       "5                 Action|Adventure|Comedy|Romance  \n",
       "6                          Action|Adventure|Drama  \n",
       "7                                    Comedy|Drama  \n",
       "8                    Animation|Children's|Musical  \n",
       "9              Adventure|Children's|Drama|Musical  \n",
       "10                   Animation|Children's|Musical  \n",
       "11                                        Musical  \n",
       "12                                          Drama  \n",
       "13                                         Comedy  \n",
       "14                                        Musical  \n",
       "15                                         Comedy  \n",
       "16                           Animation|Children's  \n",
       "17                           Animation|Children's  \n",
       "18                                          Drama  \n",
       "19                                 Comedy|Fantasy  \n",
       "20                                         Comedy  \n",
       "21                                      Animation  \n",
       "22                                  Comedy|Sci-Fi  \n",
       "23                                      Drama|War  \n",
       "24                                        Romance  \n",
       "25           Animation|Children's|Musical|Romance  \n",
       "26                Children's|Drama|Fantasy|Sci-Fi  \n",
       "27                                  Drama|Romance  \n",
       "28                                          Drama  \n",
       "29                      Animation|Comedy|Thriller  \n",
       "30                           Animation|Children's  \n",
       "31                                          Drama  \n",
       "32  Adventure|Animation|Children's|Comedy|Musical  \n",
       "33            Animation|Children's|Comedy|Musical  \n",
       "34                           Animation|Children's  \n",
       "35                   Animation|Children's|Musical  \n",
       "36                                          Drama  \n",
       "37                   Animation|Children's|Musical  \n",
       "38                                       Thriller  \n",
       "39                                          Drama  \n",
       "40                    Animation|Children's|Comedy  \n",
       "41                                          Drama  \n",
       "42                                          Drama  \n",
       "43                           Action|Crime|Romance  \n",
       "44                Action|Adventure|Fantasy|Sci-Fi  \n",
       "45                      Children's|Comedy|Musical  \n",
       "46                   Animation|Children's|Musical  \n",
       "47                                          Drama  \n",
       "48                               Action|Drama|War  \n",
       "49                               Children's|Drama  \n",
       "50                    Animation|Children's|Comedy  \n",
       "51                           Crime|Drama|Thriller  \n",
       "52                                          Drama  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_detail_user_1 = ratings[ratings['user_id']==1]\n",
    "print(f'user_1 이 시청한 영화 개수: {len(movies_detail_user_1)}')\n",
    "movies_detail_user_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 유저별 시청 영화 개수 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_counts = ratings.groupby(by=['user_id']).agg({'movie_id': 'count'}).reset_index()\n",
    "user_counts.columns = ['user_id', 'movies_count']\n",
    "\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.bar(user_counts.user_id, user_counts.movies_count, color=['r','g','b'])\n",
    "plt.xlabel('user_id')\n",
    "plt.ylabel('movies_count')\n",
    "plt.grid(ls='dotted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>user_id</th>\n",
       "      <th>movies_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4167</th>\n",
       "      <td>4169</td>\n",
       "      <td>1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4275</th>\n",
       "      <td>4277</td>\n",
       "      <td>1715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>1680</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3616</th>\n",
       "      <td>3618</td>\n",
       "      <td>1146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>1015</td>\n",
       "      <td>1145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5829</th>\n",
       "      <td>5831</td>\n",
       "      <td>1136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>2909</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>1941</td>\n",
       "      <td>1116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>424</td>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>1980</td>\n",
       "      <td>1054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>1285</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>3224</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>3539</td>\n",
       "      <td>1029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3390</th>\n",
       "      <td>3391</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3031</th>\n",
       "      <td>3032</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>1181</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3839</th>\n",
       "      <td>3841</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4342</th>\n",
       "      <td>4344</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4446</th>\n",
       "      <td>4448</td>\n",
       "      <td>970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>1088</td>\n",
       "      <td>966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>549</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4723</th>\n",
       "      <td>4725</td>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>678</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>1448</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>2063</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3291</th>\n",
       "      <td>3292</td>\n",
       "      <td>897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>1698</td>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>4808</td>\n",
       "      <td>868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>2116</td>\n",
       "      <td>855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  movies_count\n",
       "4167     4169          1968\n",
       "4275     4277          1715\n",
       "1679     1680          1515\n",
       "3616     3618          1146\n",
       "1014     1015          1145\n",
       "5829     5831          1136\n",
       "2908     2909          1119\n",
       "1940     1941          1116\n",
       "423       424          1106\n",
       "1979     1980          1054\n",
       "1284     1285          1034\n",
       "3223     3224          1034\n",
       "3538     3539          1029\n",
       "3390     3391          1012\n",
       "3031     3032          1003\n",
       "1180     1181           981\n",
       "3839     3841           976\n",
       "4342     4344           973\n",
       "4446     4448           970\n",
       "1087     1088           966\n",
       "548       549           956\n",
       "888       889           943\n",
       "4723     4725           936\n",
       "677       678           933\n",
       "1447     1448           930\n",
       "2062     2063           906\n",
       "3291     3292           897\n",
       "1697     1698           883\n",
       "4806     4808           868\n",
       "2115     2116           855"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_30 = user_counts.sort_values(by=['movies_count'], ascending=False).head(30)\n",
    "top_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>user_id</th>\n",
       "      <th>movies_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4484</th>\n",
       "      <td>4486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4347</th>\n",
       "      <td>4349</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4634</th>\n",
       "      <td>4636</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4363</th>\n",
       "      <td>4365</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>1102</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4054</th>\n",
       "      <td>4056</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>941</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5848</th>\n",
       "      <td>5850</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5826</th>\n",
       "      <td>5828</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3640</th>\n",
       "      <td>3642</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5942</th>\n",
       "      <td>5944</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2743</th>\n",
       "      <td>2744</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4545</th>\n",
       "      <td>4547</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5157</th>\n",
       "      <td>5159</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5318</th>\n",
       "      <td>5320</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2685</th>\n",
       "      <td>2686</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>559</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>3325</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>2037</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>2381</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>947</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4190</th>\n",
       "      <td>4192</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5368</th>\n",
       "      <td>5370</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>745</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>3488</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>821</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4140</th>\n",
       "      <td>4142</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>3407</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2762</th>\n",
       "      <td>2763</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>572</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  movies_count\n",
       "4484     4486             1\n",
       "4347     4349             7\n",
       "4634     4636             9\n",
       "4363     4365             9\n",
       "1101     1102             9\n",
       "4054     4056             9\n",
       "940       941            10\n",
       "5848     5850            11\n",
       "5826     5828            11\n",
       "3640     3642            11\n",
       "5942     5944            11\n",
       "2743     2744            11\n",
       "4545     4547            12\n",
       "5157     5159            12\n",
       "5318     5320            12\n",
       "2685     2686            12\n",
       "558       559            13\n",
       "3324     3325            13\n",
       "2036     2037            13\n",
       "2380     2381            13\n",
       "946       947            13\n",
       "4190     4192            13\n",
       "5368     5370            13\n",
       "744       745            13\n",
       "3487     3488            13\n",
       "820       821            13\n",
       "4140     4142            13\n",
       "3406     3407            14\n",
       "2762     2763            14\n",
       "571       572            14"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_30 = user_counts.sort_values(by=['movies_count'], ascending=True).head(30)\n",
    "bottom_30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) 분석해 봅시다.\n",
    "- ratings에 있는 유니크한 영화 개수\n",
    "- ratings에 있는 유니크한 사용자 수\n",
    "- 가장 인기 있는 영화 30개(인기순)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ratings에 있는 유니크한 영화 개수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3628"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 영화 개수\n",
    "ratings['movie_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ratings에 있는 유니크한 사용자 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6039"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 사용자 수\n",
    "ratings['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 가장 인기 있는 영화 30개(인기순)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id  title                                                \n",
       "2858      American Beauty (1999)                                   3211\n",
       "260       Star Wars: Episode IV - A New Hope (1977)                2910\n",
       "1196      Star Wars: Episode V - The Empire Strikes Back (1980)    2885\n",
       "1210      Star Wars: Episode VI - Return of the Jedi (1983)        2716\n",
       "2028      Saving Private Ryan (1998)                               2561\n",
       "589       Terminator 2: Judgment Day (1991)                        2509\n",
       "593       Silence of the Lambs, The (1991)                         2498\n",
       "1198      Raiders of the Lost Ark (1981)                           2473\n",
       "1270      Back to the Future (1985)                                2460\n",
       "2571      Matrix, The (1999)                                       2434\n",
       "480       Jurassic Park (1993)                                     2413\n",
       "2762      Sixth Sense, The (1999)                                  2385\n",
       "608       Fargo (1996)                                             2371\n",
       "110       Braveheart (1995)                                        2314\n",
       "1580      Men in Black (1997)                                      2297\n",
       "527       Schindler's List (1993)                                  2257\n",
       "1197      Princess Bride, The (1987)                               2252\n",
       "2396      Shakespeare in Love (1998)                               2213\n",
       "1617      L.A. Confidential (1997)                                 2210\n",
       "318       Shawshank Redemption, The (1994)                         2194\n",
       "858       Godfather, The (1972)                                    2167\n",
       "1265      Groundhog Day (1993)                                     2121\n",
       "1097      E.T. the Extra-Terrestrial (1982)                        2102\n",
       "2997      Being John Malkovich (1999)                              2066\n",
       "2716      Ghostbusters (1984)                                      2051\n",
       "296       Pulp Fiction (1994)                                      2030\n",
       "356       Forrest Gump (1994)                                      2022\n",
       "1240      Terminator, The (1984)                                   2019\n",
       "1         Toy Story (1995)                                         2000\n",
       "457       Fugitive, The (1993)                                     1941\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 인기 많은 영화 내림차순 30개\n",
    "movie_count = ratings.groupby(by=['movie_id', 'title'])['user_id'].count()\n",
    "movie_count.sort_values(ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) 내가 선호하는 영화를 5가지 골라서 ratings에 추가해 줍시다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 모델 검증 위한 사용자 초기 정보 세팅\n",
    "    - 추천 시스템은 처음 가입하는 사용자의 취향과 유사한 영화 정보를 5개 이상 입력받아야 함\n",
    "    - 좋아하는 영화를 기존의 데이터에 추가하기\n",
    "        - user_id: 6041  # user.dat 의 마지막 번호가 6040 이므로 추가할 user_id 값은 6041 로 설정\n",
    "        - movie_id: my_favorite.keys()  # {movie_id: movie_title} 형태의 dictionary 라서 .keys()로 입력\n",
    "            - movie_id 여야 제대로 동작함! 기존의 데이터에서 영화를 골랐기 때문!\n",
    "            - movie_title 로 입력할 경우 새로운 인덱싱이 되므로 기존의 데이터에 있는 영화와 다르게 취급됨!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 영화 제목으로 장르 추출하는 함수\n",
    "def get_genre_of_movie(my_favorite):\n",
    "    genres_of_my_favorite = []\n",
    "    for k, v in my_favorite.items():\n",
    "        genres_of_my_favorite.extend(movies[movies['title'] == v]['genre'].to_list())\n",
    "    return genres_of_my_favorite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000203</th>\n",
       "      <td>6040</td>\n",
       "      <td>1090</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-04-26 02:18:38</td>\n",
       "      <td>Platoon (1986)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>6040</td>\n",
       "      <td>1094</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:21:27</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>6040</td>\n",
       "      <td>562</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:19:06</td>\n",
       "      <td>Welcome to the Dollhouse (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>6040</td>\n",
       "      <td>1096</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:20:48</td>\n",
       "      <td>Sophie's Choice (1982)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>6040</td>\n",
       "      <td>1097</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:19:29</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>Children's|Drama|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6041</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>Action|Comedy|Crime|Horror|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6041</td>\n",
       "      <td>164</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Devil in a Blue Dress (1995)</td>\n",
       "      <td>Crime|Film-Noir|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6041</td>\n",
       "      <td>293</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Professional, The (a.k.a. Leon: The Profession...</td>\n",
       "      <td>Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6041</td>\n",
       "      <td>426</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Body Snatchers (1993)</td>\n",
       "      <td>Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6041</td>\n",
       "      <td>3476</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Jacob's Ladder (1990)</td>\n",
       "      <td>Horror|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  movie_id  counts            timestamp  \\\n",
       "1000203     6040      1090       3  2000-04-26 02:18:38   \n",
       "1000205     6040      1094       5  2000-04-25 23:21:27   \n",
       "1000206     6040       562       5  2000-04-25 23:19:06   \n",
       "1000207     6040      1096       4  2000-04-26 02:20:48   \n",
       "1000208     6040      1097       4  2000-04-26 02:19:29   \n",
       "0           6041        70       3  2022-03-02 13:06:25   \n",
       "1           6041       164       4  2022-03-02 13:06:25   \n",
       "2           6041       293       4  2022-03-02 13:06:25   \n",
       "3           6041       426       5  2022-03-02 13:06:25   \n",
       "4           6041      3476       5  2022-03-02 13:06:25   \n",
       "\n",
       "                                                     title  \\\n",
       "1000203                                     Platoon (1986)   \n",
       "1000205                            Crying Game, The (1992)   \n",
       "1000206                    Welcome to the Dollhouse (1995)   \n",
       "1000207                             Sophie's Choice (1982)   \n",
       "1000208                  E.T. the Extra-Terrestrial (1982)   \n",
       "0                               From Dusk Till Dawn (1996)   \n",
       "1                             Devil in a Blue Dress (1995)   \n",
       "2        Professional, The (a.k.a. Leon: The Profession...   \n",
       "3                                    Body Snatchers (1993)   \n",
       "4                                    Jacob's Ladder (1990)   \n",
       "\n",
       "                                       genre  \n",
       "1000203                            Drama|War  \n",
       "1000205                    Drama|Romance|War  \n",
       "1000206                         Comedy|Drama  \n",
       "1000207                                Drama  \n",
       "1000208      Children's|Drama|Fantasy|Sci-Fi  \n",
       "0        Action|Comedy|Crime|Horror|Thriller  \n",
       "1           Crime|Film-Noir|Mystery|Thriller  \n",
       "2               Crime|Drama|Romance|Thriller  \n",
       "3                     Horror|Sci-Fi|Thriller  \n",
       "4                    Horror|Mystery|Thriller  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 현재 시간\n",
    "now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "# 영화 데이터 추가! 단, 이름은 꼭 데이터셋에 있는 것과 동일하게 맞춰주세요.\n",
    "my_favorite = {\n",
    "    70: \"From Dusk Till Dawn (1996)\",\n",
    "    164: \"Devil in a Blue Dress (1995)\",\n",
    "    293: \"Professional, The (a.k.a. Leon: The Professional) (1994)\",\n",
    "    426: \"Body Snatchers (1993)\",\n",
    "    3476: \"Jacob's Ladder (1990)\",\n",
    "}\n",
    "\n",
    "# 6041이라는 user_id가 위 영화를 counts회씩 시청했다고 가정하겠습니다.\n",
    "my_playlist = pd.DataFrame({\n",
    "    'user_id': [6041]*5,\n",
    "    'movie_id': my_favorite.keys(),\n",
    "    'counts': [3,4,4,5,5],\n",
    "    'timestamp': [now]*5,\n",
    "    'title': my_favorite.values(),\n",
    "    'genre': get_genre_of_movie(my_favorite)\n",
    "})\n",
    "\n",
    "if not ratings.isin({'user_id':[6041]})['user_id'].any():  # user_id에 6041이라는 데이터가 없다면\n",
    "    ratings = ratings.append(my_playlist)  # my_favorite 데이터 추가\n",
    "\n",
    "ratings.tail(10)  # 잘 추가되었는지 확인해 봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 모델에 활용하기 위한 전처리\n",
    "    - indexing: 데이터 user 와 movie 각각에 번호 붙이기\n",
    "    - pandas.DataFrame.unique(): 특정 컬럼에 포함된 유니크한 데이터만 모아줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 고유한 유저, 영화를 찾아내는 코드\n",
    "user_unique = ratings['user_id'].unique()\n",
    "# movie_unique = ratings['movie_id'].unique()\n",
    "movie_unique = ratings['title'].unique()\n",
    "\n",
    "# 유저, 영화 indexing 하는 코드 idx는 index의 약자입니다.\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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user_id indexing: 6039\n",
      "\n",
      "movie_id indexing: 1034\n",
      "movie_id indexing: 864\n",
      "movie_id indexing: 890\n",
      "movie_id indexing: 2361\n",
      "movie_id indexing: 279\n"
     ]
    }
   ],
   "source": [
    "# 인덱싱이 잘 되었는지 확인해 봅니다. \n",
    "print('user_id indexing: {}\\n'.format(user_to_idx[6041]))\n",
    "\n",
    "for k, v in my_favorite.items():\n",
    "    print('movie_id indexing: {}'.format(movie_to_idx[v]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000203</th>\n",
       "      <td>6038</td>\n",
       "      <td>1030</td>\n",
       "      <td>3</td>\n",
       "      <td>2000-04-26 02:18:38</td>\n",
       "      <td>Platoon (1986)</td>\n",
       "      <td>Drama|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000205</th>\n",
       "      <td>6038</td>\n",
       "      <td>986</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:21:27</td>\n",
       "      <td>Crying Game, The (1992)</td>\n",
       "      <td>Drama|Romance|War</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000206</th>\n",
       "      <td>6038</td>\n",
       "      <td>311</td>\n",
       "      <td>5</td>\n",
       "      <td>2000-04-25 23:19:06</td>\n",
       "      <td>Welcome to the Dollhouse (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000207</th>\n",
       "      <td>6038</td>\n",
       "      <td>142</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:20:48</td>\n",
       "      <td>Sophie's Choice (1982)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000208</th>\n",
       "      <td>6038</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>2000-04-26 02:19:29</td>\n",
       "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
       "      <td>Children's|Drama|Fantasy|Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6039</td>\n",
       "      <td>1034</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>From Dusk Till Dawn (1996)</td>\n",
       "      <td>Action|Comedy|Crime|Horror|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6039</td>\n",
       "      <td>864</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Devil in a Blue Dress (1995)</td>\n",
       "      <td>Crime|Film-Noir|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6039</td>\n",
       "      <td>890</td>\n",
       "      <td>4</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Professional, The (a.k.a. Leon: The Profession...</td>\n",
       "      <td>Crime|Drama|Romance|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6039</td>\n",
       "      <td>2361</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Body Snatchers (1993)</td>\n",
       "      <td>Horror|Sci-Fi|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6039</td>\n",
       "      <td>279</td>\n",
       "      <td>5</td>\n",
       "      <td>2022-03-02 13:06:25</td>\n",
       "      <td>Jacob's Ladder (1990)</td>\n",
       "      <td>Horror|Mystery|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id  movie_id  counts            timestamp  \\\n",
       "1000203     6038      1030       3  2000-04-26 02:18:38   \n",
       "1000205     6038       986       5  2000-04-25 23:21:27   \n",
       "1000206     6038       311       5  2000-04-25 23:19:06   \n",
       "1000207     6038       142       4  2000-04-26 02:20:48   \n",
       "1000208     6038        26       4  2000-04-26 02:19:29   \n",
       "0           6039      1034       3  2022-03-02 13:06:25   \n",
       "1           6039       864       4  2022-03-02 13:06:25   \n",
       "2           6039       890       4  2022-03-02 13:06:25   \n",
       "3           6039      2361       5  2022-03-02 13:06:25   \n",
       "4           6039       279       5  2022-03-02 13:06:25   \n",
       "\n",
       "                                                     title  \\\n",
       "1000203                                     Platoon (1986)   \n",
       "1000205                            Crying Game, The (1992)   \n",
       "1000206                    Welcome to the Dollhouse (1995)   \n",
       "1000207                             Sophie's Choice (1982)   \n",
       "1000208                  E.T. the Extra-Terrestrial (1982)   \n",
       "0                               From Dusk Till Dawn (1996)   \n",
       "1                             Devil in a Blue Dress (1995)   \n",
       "2        Professional, The (a.k.a. Leon: The Profession...   \n",
       "3                                    Body Snatchers (1993)   \n",
       "4                                    Jacob's Ladder (1990)   \n",
       "\n",
       "                                       genre  \n",
       "1000203                            Drama|War  \n",
       "1000205                    Drama|Romance|War  \n",
       "1000206                         Comedy|Drama  \n",
       "1000207                                Drama  \n",
       "1000208      Children's|Drama|Fantasy|Sci-Fi  \n",
       "0        Action|Comedy|Crime|Horror|Thriller  \n",
       "1           Crime|Film-Noir|Mystery|Thriller  \n",
       "2               Crime|Drama|Romance|Thriller  \n",
       "3                     Horror|Sci-Fi|Thriller  \n",
       "4                    Horror|Mystery|Thriller  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing을 통해 데이터 컬럼 내 값을 바꾸는 코드\n",
    "# dictionary 자료형의 get 함수는 https://wikidocs.net/16 을 참고하세요.\n",
    "\n",
    "# user_to_idx.get을 통해 user_id 컬럼의 모든 값을 인덱싱한 Series를 구해 봅시다. \n",
    "# 혹시 정상적으로 인덱싱되지 않은 row가 있다면 인덱스가 NaN이 될 테니 dropna()로 제거합니다. \n",
    "temp_user_ratings = ratings['user_id'].map(user_to_idx.get).dropna()\n",
    "if len(temp_user_ratings) == len(ratings):   # 모든 row가 정상적으로 인덱싱되었다면\n",
    "    print('user_id column indexing OK!!')\n",
    "    ratings['user_id'] = temp_user_ratings   # ratings['user_id']을 인덱싱된 Series로 교체해 줍니다. \n",
    "else:\n",
    "    print('user_id column indexing Fail!!')\n",
    "\n",
    "# movie_to_idx을 통해 movie 컬럼도 동일한 방식으로 인덱싱해 줍니다. \n",
    "# temp_movie_ratings = ratings['movie_id'].map(movie_to_idx.get).dropna()\n",
    "temp_movie_ratings = ratings['title'].map(movie_to_idx.get).dropna()\n",
    "if len(temp_movie_ratings) == len(ratings):\n",
    "    print('movie column indexing OK!!')\n",
    "    ratings['movie_id'] = temp_movie_ratings\n",
    "else:\n",
    "    print('movie column indexing Fail!!')\n",
    "\n",
    "ratings.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 데이터 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id      0\n",
       "movie_id     0\n",
       "counts       0\n",
       "timestamp    0\n",
       "title        0\n",
       "genre        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 836483 entries, 0 to 4\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   user_id    836483 non-null  int64 \n",
      " 1   movie_id   836483 non-null  int64 \n",
      " 2   counts     836483 non-null  int64 \n",
      " 3   timestamp  836483 non-null  object\n",
      " 4   title      836483 non-null  object\n",
      " 5   genre      836483 non-null  object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 44.7+ MB\n"
     ]
    }
   ],
   "source": [
    "ratings.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 시청횟수별 데이터의 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All data: 836483\n",
      "\n",
      "Only 3 data: 261198, Ratio: 31.23%\n",
      "Only 4 data: 348973, Ratio: 41.72%\n",
      "Only 5 data: 226312, Ratio: 27.06%\n"
     ]
    }
   ],
   "source": [
    "# 총 데이터 개수\n",
    "all_data = len(ratings)\n",
    "print(f'All data: {all_data}\\n')\n",
    "\n",
    "# n회 시청한 데이터의 비율을 보는 코드\n",
    "view_counts = set(ratings['counts'])\n",
    "ratio_list = []\n",
    "for n in view_counts:\n",
    "    only_num = ratings[ratings['counts']==n]\n",
    "    len_only_num = len(only_num)  # n회 시청한 데이터 개수\n",
    "    ratio = len_only_num/all_data\n",
    "    ratio_list.append(ratio)\n",
    "\n",
    "    print(f'Only {n} data: {len_only_num}, Ratio: {ratio:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 파이 차트 그리기\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(ratio_list, autopct='%.2f%%')\n",
    "plt.title('View Ratio Pie Chart')\n",
    "plt.legend(view_counts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) CSR matrix를 직접 만들어 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<6040x3628 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 836483 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 37,
     "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_id'].nunique()\n",
    "\n",
    "csr_data = csr_matrix((ratings.counts, (ratings.user_id, ratings.movie_id)), shape= (num_user, num_movie))\n",
    "csr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6040, 3628)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) als_model = AlternatingLeastSquares 모델을 직접 구성하여 훈련시켜 봅시다.\n",
    "- [AlternatingLeastSquares](https://implicit.readthedocs.io/en/latest/als.html) 클래스의 __init__ 파라미터\n",
    "    - factors (int, optional): 유저와 아이템의 벡터를 몇 차원으로 할 것인지\n",
    "    - regularization (float, optional): 과적합을 방지하기 위해 정규화 값을 얼마나 사용할 것인지\n",
    "    - dtype (data-type, optional)\n",
    "    - use_native (bool, optional)\n",
    "    - use_cg (bool, optional)\n",
    "    - use_gpu (bool, optional): GPU를 사용할 것인지\n",
    "    - iterations (int, optional): epochs와 같은 의미. 데이터를 몇 번 반복해서 학습할 것인지\n",
    "    - calculate_training_loss (bool, optional)\n",
    "    - num_threads (int, optional)\n",
    "    - random_state (int, RandomState or None, optional)\n",
    "- factors 와 iterations 를 늘릴수록 학습 데이터를 잘 학습하게 되지만 과적합의 우려가 있으니 좋은 값을 찾아야 한다!\n",
    "    - factors=300, iterations=15 으로 진행\n",
    "- **cf. 패키지 설치 참고: 최신 implicit 패키지 버전 0.5.2 로 사용하면 에러가 발생하므로 삭제하고 lms 클라우드와 동일한 0.4.8 버전으로 설치해야 함!!!**\n",
    "\n",
    "```bash\n",
    "# implicit 패키지 버전 0.4.8로 설치해야 에러가 발생하지 않음!\n",
    "pip install implicit==0.4.8\n",
    "\n",
    "# 아나콘다 사용자일 경우 implicit 0.4.8 버전 설치 코드\n",
    "conda install -c conda-forge implicit==0.4.8\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implicit AlternatingLeastSquares 모델의 선언\n",
    "als_model = AlternatingLeastSquares(factors=300, regularization=0.01, use_gpu=False, iterations=15, dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3628x6040 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 836483 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 41,
     "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3628, 6040)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csr_data_transpose.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [00:10<00:00,  1.48it/s]\n"
     ]
    }
   ],
   "source": [
    "# 모델 훈련\n",
    "als_model.fit(csr_data_transpose)"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.28502363, -0.4773997 , -0.29852918,  0.09017462,  0.5770129 ,\n",
       "       -0.3109712 , -0.32419273, -0.3940362 ,  0.06030442,  0.5642371 ,\n",
       "        0.5806169 ,  0.20155005,  0.37006274, -0.29308242, -1.004876  ,\n",
       "        0.25679433, -0.2555344 ,  0.4708897 ,  0.17899644, -0.9408019 ,\n",
       "        0.30086213, -0.13773172, -0.77067417, -0.43851626,  0.11563419,\n",
       "       -0.04543833,  0.6713725 ,  0.3190956 ,  0.25819942, -0.1918004 ,\n",
       "        0.15769635,  0.14883788, -0.2929961 , -0.32120237,  0.3486411 ,\n",
       "       -0.1287454 ,  0.15549858, -0.29596835,  0.06204395, -0.23088716,\n",
       "       -0.04355889, -0.38206118, -0.5246314 ,  0.14434431,  0.38958123,\n",
       "        0.3309625 ,  0.72528696, -0.6280095 , -0.7455123 ,  0.28830937,\n",
       "       -0.11558612,  0.24093689,  0.04729592, -0.09140401, -0.06136812,\n",
       "       -0.59708786, -0.07929159,  0.28108752,  0.39470965, -0.2809593 ,\n",
       "        0.3425197 , -0.24044625, -0.5262361 ,  0.5631618 ,  1.0396079 ,\n",
       "        0.4421009 , -0.22549398,  0.11266248,  0.29615647,  0.32970896,\n",
       "       -0.47469056,  0.49284554, -0.07572443,  0.03251339, -0.27029923,\n",
       "        0.6261168 ,  0.06946231,  0.17053898, -0.2227713 , -0.09243032,\n",
       "       -0.70029867, -0.01671707,  0.08219235,  0.30023542, -0.1795443 ,\n",
       "        0.01175927, -0.25463727,  0.28054526, -0.46933168, -0.02456127,\n",
       "       -0.3859567 , -0.2877678 ,  0.54010713,  0.26233822,  0.46541205,\n",
       "       -0.16631696, -0.01854908,  0.39943567,  0.19376233, -0.37500674,\n",
       "       -0.50490755, -0.22509496,  0.23447175, -0.05350287, -0.15576115,\n",
       "        0.12021883, -0.46377543, -0.18879254,  0.21290092,  0.3324831 ,\n",
       "       -0.01352822,  0.11278683,  0.35773158, -0.8625184 , -0.23719233,\n",
       "       -0.3086742 , -0.10616641, -0.2992748 ,  0.10622327,  0.1233576 ,\n",
       "       -0.24093874,  0.24205506,  0.2290934 , -0.6855959 ,  0.77871096,\n",
       "        0.7368613 , -0.7356813 ,  0.2723    , -0.0243332 ,  0.3002934 ,\n",
       "        0.72999585,  0.04287915, -0.41801623,  0.2419785 ,  0.07616834,\n",
       "       -1.0598555 , -0.23754343, -0.18006127,  0.17778362, -0.58323175,\n",
       "        0.05853242, -0.09317972, -0.0868753 , -0.20933561,  0.34062737,\n",
       "       -0.11120368, -0.14914212, -0.45959094, -0.5261559 ,  0.3097596 ,\n",
       "        0.07748634, -0.6125565 ,  0.21418104, -0.13496542, -0.10116855,\n",
       "       -0.08585856,  0.41807985,  0.17350888, -0.23206702, -0.06700608,\n",
       "        0.03053894, -0.5525309 ,  0.18094566, -0.5115036 , -0.21315423,\n",
       "        0.5891707 ,  0.95311815,  0.5637904 ,  0.49152684,  0.19591497,\n",
       "       -0.5430038 , -0.17243344, -0.08483922,  0.2531489 ,  0.9071057 ,\n",
       "        0.7838743 , -1.0415037 ,  0.6007752 ,  0.08786093,  0.6639943 ,\n",
       "        0.2212427 , -0.24221574,  0.08473838,  0.18778403,  0.01446184,\n",
       "        0.62484205, -0.22567548, -0.33223763,  0.59021616, -0.22165252,\n",
       "       -0.19981763,  0.15152025, -0.32415688,  0.03600784, -0.06578467,\n",
       "        0.13409062,  0.13523494, -0.01271794, -0.01758629,  0.13601746,\n",
       "        0.09853945,  0.26852193,  0.23092961,  0.27065998,  0.03993921,\n",
       "       -0.2252801 , -0.03460058,  0.17291807, -0.07344282, -0.17420794,\n",
       "       -0.16889133, -0.42856014,  0.05197839, -0.74932057,  0.3280375 ,\n",
       "        0.05376661,  0.11385576,  0.4101306 , -0.0671078 ,  0.29566634,\n",
       "       -0.2436878 ,  0.28964278, -0.00831799, -0.2996185 ,  0.37593663,\n",
       "        0.03301599, -0.22913387,  0.07191923, -0.31984293, -0.10980678,\n",
       "        0.00823126,  0.4588105 , -0.44571915,  0.29659238, -0.15868649,\n",
       "       -0.01879673,  0.35842314, -0.1700228 ,  0.48253456, -0.04845695,\n",
       "       -0.30572918,  0.1107466 , -0.26742107, -0.2305059 ,  0.40836012,\n",
       "        0.2896591 ,  0.38994792,  0.2199309 , -0.05622251,  0.26176345,\n",
       "       -0.06327932,  0.20080082, -0.00732824,  0.00156636, -0.3913141 ,\n",
       "       -0.19957133, -0.55449104,  0.47748718,  0.1923955 ,  0.41713208,\n",
       "        0.03578021, -0.44979015, -0.2975339 , -0.3276392 ,  0.03159164,\n",
       "        0.07405443,  0.17384072, -0.10837056, -0.09478875, -0.06678662,\n",
       "        0.43868586, -0.5689882 ,  0.03125532,  0.48042184, -0.4059976 ,\n",
       "       -0.2268434 ,  0.7101784 , -0.14042644, -0.00424548, -0.03761283,\n",
       "       -0.21935277, -0.19612855, -0.09876446, -0.05513571, -0.02069658,\n",
       "        0.11126625, -0.24919201,  0.15026625,  0.05705695, -0.07095835,\n",
       "       -0.51762336, -0.2418296 , -0.2766394 , -0.09924189,  0.19064896,\n",
       "        0.3469407 ,  0.34462446,  0.40534455,  0.05588439,  0.17773427],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = user_to_idx[6041]\n",
    "user_vector = als_model.user_factors[user]\n",
    "user_vector  # 사용자 벡터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.25756338e-02,  6.77220349e-04, -3.10079195e-03,  1.67911127e-02,\n",
       "        2.65094149e-03, -1.36612765e-02,  1.21771768e-02, -1.44103244e-02,\n",
       "        2.16685981e-03, -8.55445582e-03,  7.68906251e-03,  1.72433350e-02,\n",
       "        8.05560034e-03,  1.49913980e-02, -1.75047871e-02,  5.74991666e-03,\n",
       "       -1.93641502e-02,  5.22461766e-03, -3.16584297e-02, -9.34082177e-03,\n",
       "        1.63117275e-02,  1.15973298e-02, -6.07063528e-03,  6.02732354e-04,\n",
       "        1.56719983e-02,  2.29272917e-02,  3.39963324e-02,  1.74624491e-02,\n",
       "        3.14054720e-04, -3.63196270e-03, -1.32214166e-02,  2.04146048e-03,\n",
       "       -2.15351637e-02,  1.09737786e-02,  1.82446018e-02,  2.44474392e-02,\n",
       "       -2.10786164e-02, -5.17288968e-03, -5.98888006e-03,  1.06463004e-02,\n",
       "        1.27985636e-02,  8.13888106e-03,  3.33775468e-02, -6.70570647e-03,\n",
       "        3.51851340e-03,  1.66506637e-02, -2.49175238e-03,  9.50514711e-03,\n",
       "        2.01143622e-02,  1.75296341e-03,  1.14870276e-02, -2.42099632e-03,\n",
       "       -1.60673410e-02, -1.66724194e-02,  1.55029744e-02, -8.30689725e-03,\n",
       "        1.85530875e-02, -6.01297244e-03,  6.32601324e-03,  8.78406968e-03,\n",
       "        2.94474028e-02,  2.68951757e-03,  2.70302244e-03,  6.46137120e-03,\n",
       "       -2.16584001e-02,  6.43358612e-03, -1.37152886e-02,  1.36993434e-02,\n",
       "        5.02626505e-03,  1.35117341e-02,  1.08436719e-02, -5.81371551e-03,\n",
       "        9.29498300e-03, -2.33871974e-02,  1.16979778e-02, -1.38001051e-02,\n",
       "       -1.42264850e-02,  2.13649478e-02, -6.27232157e-03, -3.60781793e-03,\n",
       "        4.04539816e-02,  4.63039381e-03,  1.10984358e-04, -1.74009260e-02,\n",
       "        5.30015118e-03,  2.29112953e-02,  4.02919995e-03,  3.36058112e-03,\n",
       "       -3.50190094e-03,  9.52652749e-03,  2.86252927e-02,  1.18569024e-02,\n",
       "        1.30552035e-02,  1.33335125e-02,  3.34981713e-03,  1.30974157e-02,\n",
       "       -4.58048307e-04,  1.31727122e-02,  1.99884456e-02,  3.12645501e-03,\n",
       "       -1.04872454e-02,  2.17251163e-02,  2.76263468e-02,  9.16968845e-03,\n",
       "        6.10567909e-03, -1.37313353e-02,  6.64394815e-04, -1.23531790e-02,\n",
       "        1.12576028e-02,  1.13069191e-05,  3.30777769e-03,  4.55268240e-03,\n",
       "        9.08783358e-03,  1.54858893e-02, -2.03567762e-02,  1.18171247e-02,\n",
       "        1.74133815e-02,  3.43510136e-02,  8.52828659e-03,  2.17407774e-02,\n",
       "        1.95693579e-02,  1.29322885e-02, -1.27297908e-03,  1.77501573e-03,\n",
       "        6.78841351e-03,  4.15150030e-03,  1.37048736e-02, -1.42632155e-02,\n",
       "       -4.95273298e-07, -2.35452503e-03, -2.79828231e-03, -3.97930341e-03,\n",
       "        9.01231728e-03, -2.08089277e-02,  1.68188643e-02,  1.83280725e-02,\n",
       "       -1.48987174e-02, -5.50047774e-03,  9.32443072e-04, -6.91942638e-04,\n",
       "       -2.02253200e-02,  2.21841466e-02, -1.12050921e-02,  2.88923979e-02,\n",
       "        2.03286223e-02,  4.53384034e-03,  8.87399307e-04,  1.66150220e-02,\n",
       "        2.54171509e-02,  6.73822034e-03,  4.34457231e-03,  9.99995321e-03,\n",
       "        7.22107827e-04, -1.22427056e-02,  2.22132299e-02,  2.98547861e-03,\n",
       "       -2.39491258e-02,  1.68141425e-02,  1.42953489e-02, -1.21178916e-02,\n",
       "        2.01360956e-02,  1.95987262e-02, -8.56837817e-03, -4.91872942e-03,\n",
       "        2.32241601e-02, -2.34641042e-02,  6.60446880e-04,  2.53893062e-02,\n",
       "        2.44687367e-02,  7.43701169e-03,  8.49023741e-03,  2.42348332e-02,\n",
       "        6.83126226e-03,  2.54614763e-02, -2.20442899e-02,  2.15252421e-05,\n",
       "        6.49831863e-03,  1.50993494e-02,  3.11785024e-02, -1.56163573e-02,\n",
       "       -1.15617365e-02,  1.77631155e-02,  1.61864627e-02, -3.34770186e-03,\n",
       "       -4.82437061e-03,  1.20901549e-02, -1.16575286e-02, -1.21060135e-02,\n",
       "        1.75031610e-02,  2.00467799e-02,  2.09600888e-02,  4.57077986e-03,\n",
       "        6.27729483e-03, -2.46711615e-02, -1.08243395e-02, -2.17423099e-03,\n",
       "        2.59709060e-02,  1.48084490e-02, -1.57054868e-02,  3.14659625e-02,\n",
       "        1.21469339e-02, -7.86174182e-03,  1.34738032e-02, -2.35293768e-02,\n",
       "       -7.84020126e-03,  2.02848110e-02, -5.53406600e-04,  2.87705171e-03,\n",
       "       -3.31195071e-03, -5.55771589e-03,  4.08544531e-03,  9.62801650e-03,\n",
       "        1.88216344e-02, -2.41607279e-02,  1.53253432e-02,  1.79502629e-02,\n",
       "       -6.09490834e-03, -1.61827002e-02, -9.86316148e-03, -4.36841714e-04,\n",
       "        1.30931884e-02,  2.68321484e-02,  8.72238725e-03, -1.55335413e-02,\n",
       "        1.39049627e-02, -1.61621757e-02, -2.33038771e-03,  1.82260890e-02,\n",
       "        8.05113930e-03,  1.96225755e-02,  2.39893757e-02,  1.86999291e-02,\n",
       "        1.07446238e-02,  2.73470953e-02, -2.38470566e-02,  6.32523047e-03,\n",
       "       -1.97735112e-02,  2.34936830e-02,  4.06603003e-03,  1.27877854e-02,\n",
       "       -7.14809680e-03, -7.46245170e-03, -2.15707882e-03, -3.49650485e-03,\n",
       "       -1.17875692e-02,  2.84869857e-02, -2.59678578e-03, -2.46211723e-03,\n",
       "        1.40850805e-02, -1.06593929e-02, -1.06251137e-02,  3.71242389e-02,\n",
       "       -2.54458864e-03,  1.05601251e-02,  1.77119244e-02,  7.82279577e-03,\n",
       "        2.58890055e-02,  2.59275865e-02,  1.33478967e-02,  2.14406606e-02,\n",
       "        6.05860353e-03, -1.07584577e-02,  3.61897983e-04, -7.46236509e-03,\n",
       "       -3.06597771e-03,  5.19991340e-03, -6.55606901e-03,  3.33405402e-03,\n",
       "       -4.04372229e-04,  2.58545931e-02, -3.69435851e-03, -1.71631277e-02,\n",
       "        6.17539603e-03,  1.39401322e-02, -4.80568549e-03,  7.10303476e-03,\n",
       "        1.55460427e-03,  1.77344922e-02,  4.68259724e-03,  1.43368188e-02,\n",
       "       -5.94297017e-04, -6.44452032e-03,  4.22212575e-03,  2.27115843e-02,\n",
       "       -3.34269297e-03,  2.24030074e-02,  3.59042399e-02, -5.25755575e-03,\n",
       "        8.99117626e-03,  2.45717261e-02,  6.94952020e-03,  1.14789996e-02,\n",
       "        1.69313308e-02,  1.34063251e-02, -1.62230898e-02,  1.12205576e-02,\n",
       "        2.25939676e-02,  5.29385591e-03, -1.22138560e-02, -9.47822165e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "star_wars_epi_4 = movie_to_idx['Star Wars: Episode IV - A New Hope (1977)']\n",
    "star_wars_epi_4_vector = als_model.item_factors[star_wars_epi_4]\n",
    "star_wars_epi_4_vector  # 영화 벡터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.052471306"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# user와 star_wars_epi_4를 내적하는 코드\n",
    "np.dot(user_vector, star_wars_epi_4_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.040521607"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toy_story_2 = movie_to_idx['Toy Story 2 (1999)']\n",
    "toy_story_2_vector = als_model.item_factors[toy_story_2]\n",
    "np.dot(user_vector, toy_story_2_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_my_preference(user_vector, title, print_flag=True):\n",
    "    movie = movie_to_idx.get(title, 0)\n",
    "    movie_vector = als_model.item_factors[movie]\n",
    "\n",
    "    # user_vector와 movie_vector를 내적하는 코드\n",
    "    dot_user_movie = np.dot(user_vector, movie_vector)\n",
    "    if print_flag:\n",
    "        print(f'* user_vector 와 movie_vector({title}) 를 내적한 결과: {dot_user_movie:.2f}')\n",
    "    else:\n",
    "        return round(dot_user_movie, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* user_vector 와 movie_vector(Toy Story 2 (1999)) 를 내적한 결과: -0.04\n"
     ]
    }
   ],
   "source": [
    "predict_my_preference(user_vector, 'Toy Story 2 (1999)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* user_vector 와 movie_vector(From Dusk Till Dawn (1996)) 를 내적한 결과: 0.46\n",
      "* user_vector 와 movie_vector(Devil in a Blue Dress (1995)) 를 내적한 결과: 0.45\n",
      "* user_vector 와 movie_vector(Professional, The (a.k.a. Leon: The Professional) (1994)) 를 내적한 결과: 0.71\n",
      "* user_vector 와 movie_vector(Body Snatchers (1993)) 를 내적한 결과: 0.36\n",
      "* user_vector 와 movie_vector(Jacob's Ladder (1990)) 를 내적한 결과: 0.62\n"
     ]
    }
   ],
   "source": [
    "for movie_id, movie_title in my_favorite.items():\n",
    "    predict_my_preference(user_vector, movie_title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 영화 데이터에 선호도 컬럼 추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_prefer = []\n",
    "for title in movies['title'].to_list():\n",
    "    my_prefer.append(predict_my_preference(user_vector, title, False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "      <th>my_prefer</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",
       "      <td>-0.02</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",
       "      <td>-0.01</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",
       "      <td>-0.00</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",
       "      <td>0.07</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",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3948</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3883 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                               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",
       "3878      3948             Meet the Parents (2000)   \n",
       "3879      3949          Requiem for a Dream (2000)   \n",
       "3880      3950                    Tigerland (2000)   \n",
       "3881      3951             Two Family House (2000)   \n",
       "3882      3952               Contender, The (2000)   \n",
       "\n",
       "                             genre  my_prefer  \n",
       "0      Animation|Children's|Comedy      -0.02  \n",
       "1     Adventure|Children's|Fantasy      -0.01  \n",
       "2                   Comedy|Romance      -0.00  \n",
       "3                     Comedy|Drama       0.07  \n",
       "4                           Comedy      -0.03  \n",
       "...                            ...        ...  \n",
       "3878                        Comedy      -0.02  \n",
       "3879                         Drama      -0.03  \n",
       "3880                         Drama      -0.03  \n",
       "3881                         Drama       0.01  \n",
       "3882                Drama|Thriller       0.02  \n",
       "\n",
       "[3883 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.insert(len(movies.columns), 'my_prefer', my_prefer, allow_duplicates=True)\n",
    "movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 선호도 0.6 이상인 영화 목록"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "      <th>my_prefer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>293</td>\n",
       "      <td>Professional, The (a.k.a. Leon: The Profession...</td>\n",
       "      <td>Crime|Drama|Romance|Thriller</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>3476</td>\n",
       "      <td>Jacob's Ladder (1990)</td>\n",
       "      <td>Horror|Mystery|Thriller</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                                              title  \\\n",
       "290        293  Professional, The (a.k.a. Leon: The Profession...   \n",
       "3407      3476                              Jacob's Ladder (1990)   \n",
       "\n",
       "                             genre  my_prefer  \n",
       "290   Crime|Drama|Romance|Thriller       0.71  \n",
       "3407       Horror|Mystery|Thriller       0.62  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[movies['my_prefer'] >= 0.6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) 내가 좋아하는 영화와 비슷한 영화를 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(868, 1.0000001),\n",
       " (2415, 0.49513078),\n",
       " (1298, 0.4708036),\n",
       " (274, 0.46541053),\n",
       " (1768, 0.4537564),\n",
       " (2522, 0.45109302),\n",
       " (3217, 0.44895703),\n",
       " (313, 0.44810748),\n",
       " (2516, 0.4477871),\n",
       " (1934, 0.44736117),\n",
       " (3128, 0.4465272),\n",
       " (2934, 0.44625357),\n",
       " (3369, 0.4444599),\n",
       " (2552, 0.44382775),\n",
       " (2894, 0.443391)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favorite_movie = 'Heavenly Creatures (1994)'\n",
    "movie_title = movie_to_idx[favorite_movie]\n",
    "similar_movies = als_model.similar_items(movie_title, N=15)\n",
    "similar_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Heavenly Creatures (1994)',\n",
       " 'Trust (1990)',\n",
       " 'Flirt (1995)',\n",
       " 'All Over Me (1997)',\n",
       " 'Naked (1993)',\n",
       " 'Mute Witness (1994)',\n",
       " 'Zed & Two Noughts, A (1985)',\n",
       " 'Nowhere (1997)',\n",
       " 'Peeping Tom (1960)',\n",
       " 'Doom Generation, The (1995)',\n",
       " 'Cyclo (1995)',\n",
       " 'Sleepaway Camp (1983)',\n",
       " 'Cure, The (1995)',\n",
       " 'Normal Life (1996)',\n",
       " 'When Night Is Falling (1995)']"
      ]
     },
     "execution_count": 55,
     "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",
    "[idx_to_movie[i[0]] for i in similar_movies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "슝=3\n"
     ]
    }
   ],
   "source": [
    "def get_similar_movie(movie_name: str):\n",
    "    movie_title = movie_to_idx[movie_name]\n",
    "    similar_movie = als_model.similar_items(movie_title, N=15)\n",
    "    similar_movie = [idx_to_movie[i[0]] for i in similar_movies]\n",
    "    return similar_movie\n",
    "\n",
    "print(\"슝=3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Heavenly Creatures (1994)',\n",
       " 'Trust (1990)',\n",
       " 'Flirt (1995)',\n",
       " 'All Over Me (1997)',\n",
       " 'Naked (1993)',\n",
       " 'Mute Witness (1994)',\n",
       " 'Zed & Two Noughts, A (1985)',\n",
       " 'Nowhere (1997)',\n",
       " 'Peeping Tom (1960)',\n",
       " 'Doom Generation, The (1995)',\n",
       " 'Cyclo (1995)',\n",
       " 'Sleepaway Camp (1983)',\n",
       " 'Cure, The (1995)',\n",
       " 'Normal Life (1996)',\n",
       " 'When Night Is Falling (1995)']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_similar_movie('Deep Impact (1998)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) 내가 가장 좋아할 만한 영화들을 추천받아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(831, 0.2586314),\n",
       " (456, 0.20997207),\n",
       " (1368, 0.20478493),\n",
       " (1090, 0.18801789),\n",
       " (299, 0.18583962),\n",
       " (997, 0.18345392),\n",
       " (467, 0.1562444),\n",
       " (1070, 0.15528165),\n",
       " (855, 0.15031491),\n",
       " (1113, 0.14928639),\n",
       " (373, 0.14691266),\n",
       " (924, 0.14686173),\n",
       " (1450, 0.14364569),\n",
       " (814, 0.14088926),\n",
       " (174, 0.1408778),\n",
       " (93, 0.13864477),\n",
       " (1107, 0.13787723),\n",
       " (637, 0.13776878),\n",
       " (1200, 0.13414526),\n",
       " (239, 0.13218693)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user = user_to_idx[6041]\n",
    "# recommend에서는 user*item CSR Matrix를 받습니다.\n",
    "movie_recommended = als_model.recommend(user, csr_data, N=20, filter_already_liked_items=True)\n",
    "movie_recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Devil's Advocate, The (1997)\",\n",
       " 'Arachnophobia (1990)',\n",
       " 'Henry: Portrait of a Serial Killer (1990)',\n",
       " 'Nikita (La Femme Nikita) (1990)',\n",
       " 'Mariachi, El (1992)',\n",
       " 'Scream (1996)',\n",
       " 'Cape Fear (1991)',\n",
       " 'Long Kiss Goodnight, The (1996)',\n",
       " 'Grifters, The (1990)',\n",
       " 'Dead Again (1991)',\n",
       " 'Heat (1995)',\n",
       " 'Dark City (1998)',\n",
       " 'Big Kahuna, The (2000)',\n",
       " 'Big Daddy (1999)',\n",
       " 'Blade (1998)',\n",
       " 'Alien: Resurrection (1997)',\n",
       " \"Rosemary's Baby (1968)\",\n",
       " 'Abyss, The (1989)',\n",
       " 'American President, The (1995)',\n",
       " 'Faculty, The (1998)']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[idx_to_movie[i[0]] for i in movie_recommended]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_movie = movie_to_idx['Kansas City (1996)']\n",
    "explain = als_model.explain(user, csr_data, itemid=item_movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Devil in a Blue Dress (1995)', 0.013822216551633678),\n",
       " (\"Jacob's Ladder (1990)\", 0.0035887370886665804),\n",
       " ('Body Snatchers (1993)', 0.0034424099868628462),\n",
       " ('From Dusk Till Dawn (1996)', 0.0002720589646487189),\n",
       " ('Professional, The (a.k.a. Leon: The Professional) (1994)',\n",
       "  -0.009198419141985924)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(idx_to_movie[i[0]], i[1]) for i in explain[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 회고\n",
    "\n",
    "### - 이번 프로젝트에서 **어려웠던 점**.\n",
    "초반에 lms 코드를 그대로 가져다 썼을 때, 이상하게 로컬에서도 코랩에서도 오류가 발생해서 화가 치밀었다. als_model 훈련이 끝나고 user_vector 를 구하기 위한 als_model.user_factors[user] 코드에서 에러가 발생했다. 아무리 생각해도 오류날 곳이 아닌데 거기서 막혀서 머리가 아팠다. 알고 보니 implicit 패키지 버전이 달라서 이런 일이 발생했던 것이었다.. ^^\n",
    "\n",
    "### - 프로젝트를 진행하면서 **알아낸 점** 혹은 **아직 모호한 점**.\n",
    "1. 이해할 수 없는 오류가 가로막는다면 lms 클라우드 패키지와 똑같은 버전으로 설치해야 한다.\n",
    "2. [implicit 라이브러리 0.4.8](https://implicit.readthedocs.io/en/latest/)\n",
    "    - Fast Python Collaborative Filtering for Implicit Datasets\n",
    "    - This project provides fast Python implementations of several different popular recommendation algorithms for implicit\n",
    "3. [희소행렬(Sparse Matrix)](https://ko.wikipedia.org/wiki/%ED%9D%AC%EC%86%8C%ED%96%89%EB%A0%AC) <-> 밀집행렬(Dense Matrix)<br>![](https://wikimedia.org/api/rest_v1/media/math/render/svg/dbbd270e3e174daa36d9c2460211fe6f8569c0e6)\n",
    "    - 행렬의 값이 대부분 0인 경우\n",
    "    - 희소행렬의 자료구조 저장법: Dictonary of keys, LIL, COO, CSR or CRS, CSC or CCS\n",
    "4. CSR Matrix<br>![](https://wikimedia.org/api/rest_v1/media/math/render/svg/0cab70edf680e9e5ca0dea1a64890ac7ded25172)\n",
    "    - 가로의 순서대로 재정렬하는 방법으로 행에 관여하여 정리 압축한 것\n",
    "    - 행압축정보의 배열은 [최초시작행번호,시작행에서의 데이타 개수,두번째 행에서의 데이타 누적 개수,...,마지막행에서의 데이타 누적개수]\n",
    "5. [추천 알고리즘](https://yeomko.tistory.com/3)\n",
    "    - Contents Based Filtering\n",
    "        - e.g. 사용자 혹은 아이템에 대한 프로필 데이터를 가지고 내가 좋아했던 아이템과 비슷한 유형의 아이템을 추천하거나 나와 비슷한 유형의 사람이 좋아하는 아이템을 추천\n",
    "        - user-based recommendation, item-based recommendation\n",
    "        - 단점: 데이터셋 구성하기 어려움(많은 시간, 비용), 주관성 개입 문제(=추천의 객관성 하락)\n",
    "    - Collaborative Filtering\n",
    "        - e.g. 내가 남긴 평점 데이터를 가지고 나와 취향이 비슷한 사람이 선호하는 아이템을 추천\n",
    "        - Collaborative Filtering 문제를 푸는 세부 알고리즘: Matrix Factorization, Alternating Least Squares\n",
    "        - 장점: 도메인 제약없이 데이터셋 쌓기 쉬움, Contents Based Filtering 보다 더 정확함\n",
    "        - 단점: 신규 사용자의 경우 관찰된 행동 데이터가 적어서 추천 정확도가 급격히 떨어짐(=Cold start 문제)\n",
    "    - Hybrid Filtering\n",
    "        - e.g. 둘 다 문제가 있으니, 이 둘을 섞어서 사용해보자!\n",
    "        - Combining Filtering 기법, Collaboration via Content 기법\n",
    "6. [Collaborative Filtering](https://yeomko.tistory.com/6)\n",
    "    - X축이 사용자, Y축이 아이템인 행렬로 표기가 가능<br>![](https://t1.daumcdn.net/cfile/tistory/9995253B5C8FC8EE06)\n",
    "        - 주어진 평점 데이터 만으로 아직 평점을 남기지 않은 아이템들에 대한 평점을 예측하는 기법\n",
    "        - 평점 데이터: Explicit Dataset, Implicit Dataset\n",
    "            - Explicit Dataset\n",
    "                - 선호와 비선호를 명확하게 구분해준 데이터 셋\n",
    "                - ? 에 해당하는 데이터들을 제외한 평점 데이터 만으로 사용자의 선호도를 학습\n",
    "                - Neighborhood 기법: 평점 데이터를 가지고 양의 선형 관계 혹은 음의 선형 관계를 계산\n",
    "            - Implicit Dataset\n",
    "                - 호불호 구분 없이 사용자가 아이템을 얼마나 소비하였는지를 기록한 데이터 셋\n",
    "                - ? 에 해당하는 영역 역시 데이터로 포함하여 사용자의 선호도를 학습\n",
    "                - Latent Factor Model: 관찰된 데이터와 잠재되어 있는 데이터를 연결시키는 기법\n",
    "                    - 커다란 평점 행렬을 사용자와 아이템 Latent Factor로 분해하고, 이를 각각 학습시키는 Matrix Factorization 기법이 적합\n",
    "7. [Matrix Factorization](https://yeomko.tistory.com/5)\n",
    "    - 평점 행렬을 사용자와 아이템 Latent Factor 행렬로 분해<br>![](https://t1.daumcdn.net/cfile/tistory/99A2523D5C910CD90B)\n",
    "        - R: 사용자가 아이템에 남긴 평점을 행렬로 나타낸 것\n",
    "            - R의 크기 = 사용자 수(Nu) x 아이템의 수(Ni)\n",
    "        - Nf: Matrix Factorization 학습 시에 정하는 임의의 차원 수\n",
    "            - 개발자가 조정 가능 => 보통 50에서 200 사이로 설정\n",
    "            - X와 Y는 사용자와 아이템의 Latent Factor 행렬을 나타냄 => 우리가 학습시키고자 하는 대상\n",
    "            - 이 행렬들의 값은 아주 작은 랜덤한 값들로 초기화\n",
    "        - xu: 사용자 특징 벡터(Nf x 1 크기의 열 벡터 (column vector)) => 특정 사용자의 Latent Vector\n",
    "        - yi: 아이템 특징 벡터(Nf x 1 크기의 열 벡터 (column vector)) => 특정 아이템의 Latent Vector\n",
    "    - 분해된 행렬을 다시 곱하여 예측 평점 행렬을 계산<br>![](https://t1.daumcdn.net/cfile/tistory/990FBA3A5C910D0A1A)\n",
    "        - 평점 예측 행렬 = X 행렬의 전치행렬(Transpose) * Y 행렬\n",
    "            - X 행렬의 전치행렬(Transpose) = Nu x Nf 크기 행렬\n",
    "            - 평점 예측 행렬 = Nu x Ni 크기 행렬\n",
    "        - 평점 계산 = 특정 사용자의 Latent Vector와 특정 아이템의 Latent Vector를 곱함\n",
    "    - Loss Function\n",
    "    - Optimization\n",
    "        - Gradient Descent: loss를 미분하고, 여기에 learning rate를 곱한 다음 X와 Y 행렬의 값들을 업데이트 해주는 방식\n",
    "        - Alternating Least Squares: X와 Y 둘 중 하나를 고정시키고 다른 하나를 최적화 -> 번갈아가며 반복 -> 짧은 시간 내에 최적의 X와 Y를 찾아냄\n",
    "        - Non-convex problem(NP 문제)는 ALS 알고리즘이 적합하다.\n",
    "8. [ALS(Alternating Least Square)]((https://yeomko.tistory.com/4))<br>![](https://miro.medium.com/max/1400/1*ezY_g30VQ8MTGpDwd3z56w.png)\n",
    "    - 사용자와 아이템의 Latent Factor를 한번 씩 번갈아가며 학습<br>![](https://t1.daumcdn.net/cfile/tistory/997CAE475C8A5B7A04)\n",
    "    - Loss Function: ![](https://t1.daumcdn.net/cfile/tistory/996B484C5C87DA353A)\n",
    "    - 사용자 혹은 아이템의 Latent Factor 행렬을 아주 작은 랜덤 값으로 초기화 -> 둘 중 하나를 상수처럼 고정시켜 Loss Function을 Convex Function으로 생성 -> 미분한 다음, 미분 값을 0으로 만드는 사용자 혹은 아이템의 Latent Factor 행렬 계산 -> 이 과정을 사용자 한 번, 아이템 한 번 반복 -> 최적의 X, Y 찾기\n",
    "    - 논문에서 설정한 반복 횟수 10 ~ 15번 => 데이터 양/Sparse 정도에 따라서 조절해야 함\n",
    "    - [구현](https://yeomko.tistory.com/8) 단계: 학습 파라미터 초기화 -> 학습용 평점 데이터 설정 -> 사용자와 아이템의 Latent Factor Matrix 초기화, 선호도 행렬 P 설정 -> 신뢰도 행렬 C 설정 -> Loss Function 설정 -> Optimizer 설정 -> 학습 -> 학습 결과 분석 및 시각화\n",
    "9. [추천 엔진 성능 지표](https://yeomko.tistory.com/32)\n",
    "    - 추천 엔진 정확도 평가하기 위한 요소 2개: 순서(Ranking), 개인화(Personalization)\n",
    "    - 성능 평가 지표: mAP, NDCG, Entropy Diversity 등\n",
    "        - mAP(Mean Average Precision): recall의 값이 0부터 1까지 변화할 때 precision 값의 평균 값\n",
    "        - nDCG(normalized Discounted Cumulative Gain): 추천의 순서에 더 가중치를 두어서 성능을 평가하는 지표, 1에 가까울수록 좋음\n",
    "        - Entropy Diversity: 추천 결과의 다양성(=얼마나 분산되어 있는가)을 측정하는 지표\n",
    "cf. 성능 평가 지표(Metrics)\n",
    "    - Classification: accuracy, precision, recall, f1-score\n",
    "    - Object Detection: mAP\n",
    "    - Regression: r2-score\n",
    "\n",
    "\n",
    "### - 루브릭 평가 지표를 맞추기 위해 **시도한 것들**.\n",
    "\n",
    ">#### **루브릭**\n",
    ">|번호|평가문항|상세기준|\n",
    ">|:---:|---|---|\n",
    ">|1|CSR matrix가 정상적으로 만들어졌다.|사용자와 아이템 개수를 바탕으로 정확한 사이즈로 만들었다.|\n",
    ">|2|MF 모델이 정상적으로 훈련되어 그럴듯한 추천이 이루어졌다.|사용자와 아이템 벡터 내적수치가 의미있게 형성되었다.|\n",
    ">|3|비슷한 영화 찾기와 유저에게 추천하기의 과정이 정상적으로 진행되었다.|MF모델이 예측한 유저 선호도 및 아이템간 유사도, 기여도가 의미있게 측정되었다.|\n",
    "\n",
    "CSR Matrix 를 사용자(user_id 유니크 개수: 6040)와 아이템 개수(movie_id 유니크 개수: 3628)를 바탕으로 만들었다. CSR Matrix 를 transpose 한 값을 als_model 을 훈련할 때 사용했다.\n",
    "\n",
    "파라미터는 factors=500, iterations=30 으로 수정하고 나머지는 건드리지 않고 훈련을 진행했다. 사용자(user_id == 6041)와 아이템(movie_id) 벡터 내적수치를 각 영화마다 구해서 0.6 이상인 영화 목록을 출력해서 확인했다.\n",
    "\n",
    "유저 선호도, 아이템간 유사도, 기여도를 측정해서 비슷한 영화를 찾아서 유저에게 추천해보았다. \n",
    "\n",
    "\n",
    "### - 만약에 루브릭 평가 관련 지표를 **달성 하지 못했을 때, 이유에 관한 추정**.\n",
    "없음\n",
    "\n",
    "### - **자기 다짐**\n",
    "앞으로 LMS 클라우드에 설치된 패키지 버전과 똑같은 버전으로 설치해야 겠다.\n",
    "\n",
    "---\n",
    "- 대나무숲\n",
    "    - lms 에서는 제대로 돌아가는 코드가 로컬과 코랩 모두 돌아가질 않아서 정말 미치는 줄 알았다!!! **als_model.user_factors[user] 이 코드가 자꾸 에러를 뿜어댔다.** 원인은 lms 클라우드에 설치된 implicit 패키지 버전(0.4.8)과 내가 설치한 implicit 패키지의 버전(0.5.2)이 똑같지 않아서 해당 코드가 말썽이었던 것이었다. **0.5.2 버전의 implicit 패키지를 삭제하고 0.4.8 버전으로 설치했더니 그 오류에서 벗어날 수 있었다.** 덕분에 나를 오늘도 분노로 가득차게 만들었다. 그거 해결하고 그래프 그려보다가 끝나버렸다. 다른 걸 시도해보지 못한 점이 아쉽지만 어쩔 수 없다."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b3ce9abe337a9e694d01ea52d504102083454ad8bd4b0e3a574e4432f4229329"
  },
  "kernelspec": {
   "display_name": "Python 3.8.12 ('aiffel_3.8')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}