컨텐츠_기반_추천_알고리즘_2(0315).ipynb

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      "authorship_tag": "ABX9TyO+9KR2UdXmtrjP4udFfFHd",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cnddlf/2022_1_Study_AI/blob/main/%EC%BB%A8%ED%85%90%EC%B8%A0_%EA%B8%B0%EB%B0%98_%EC%B6%94%EC%B2%9C_%EC%95%8C%EA%B3%A0%EB%A6%AC%EC%A6%98_2(0315).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyVteueE6Bcq",
        "outputId": "0b9b7dd5-001e-42a6-c0a0-3c1cf770172c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "movies = pd.read_csv('/content/drive/MyDrive/2022_1_Study_AI/2022_1_Study_AI/data/movie/tmdb_5000_movies.csv')\n",
        "movies.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FM175MhJ6Jms",
        "outputId": "a997316a-c9ee-4051-f989-2d5536716cd9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies = movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity', 'keywords', 'overview']]"
      ],
      "metadata": {
        "id": "9ZJGdgXG6vZl"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies.dropna(inplace=True)\n",
        "df_movies.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdYBLtup6mUP",
        "outputId": "51793cc8-54a1-4161-dfdf-734f3bb47938"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4800 entries, 0 to 4802\n",
            "Data columns (total 8 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   id            4800 non-null   int64  \n",
            " 1   title         4800 non-null   object \n",
            " 2   genres        4800 non-null   object \n",
            " 3   vote_average  4800 non-null   float64\n",
            " 4   vote_count    4800 non-null   int64  \n",
            " 5   popularity    4800 non-null   float64\n",
            " 6   keywords      4800 non-null   object \n",
            " 7   overview      4800 non-null   object \n",
            "dtypes: float64(2), int64(2), object(4)\n",
            "memory usage: 337.5+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: 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 func(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval\n",
        "\n",
        "df_movies['genres']=df_movies['genres'].apply(literal_eval)\n",
        "print(df_movies['genres'][:1][0][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCtd5Uv_7ThM",
        "outputId": "6921d493-148d-4631-830c-468e46e95780"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': 28, 'name': 'Action'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies['genres'] = df_movies['genres'].apply(lambda x : [y['name'] for y in x])\n",
        "df_movies['genres'][:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sILZk3wB7ZwD",
        "outputId": "1eacb5eb-acd9-4674-c2c5-c5fdd33ec24b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [Action, Adventure, Fantasy, Science Fiction]\n",
              "1                     [Adventure, Fantasy, Action]\n",
              "Name: genres, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import numpy as np\n",
        "\n",
        "df_movies['genres_literal'] = df_movies['genres'].apply(lambda x : (' ').join(x))\n",
        "print(type(df_movies['genres_literal'][0]))\n",
        "df_movies['genres_literal'][0]\n",
        "\n",
        "countVect = CountVectorizer(ngram_range=(1,2)).fit(df_movies['genres_literal'])\n",
        "countVect.vocabulary_"
      ],
      "metadata": {
        "id": "Izlsi2f97kd3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a482de7-ced3-4de2-f0de-8516f1d7d725"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: 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",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'action': 0,\n",
              " 'adventure': 16,\n",
              " 'fantasy': 124,\n",
              " 'science': 232,\n",
              " 'fiction': 138,\n",
              " 'action adventure': 1,\n",
              " 'adventure fantasy': 24,\n",
              " 'fantasy science': 135,\n",
              " 'science fiction': 233,\n",
              " 'fantasy action': 125,\n",
              " 'crime': 64,\n",
              " 'adventure crime': 20,\n",
              " 'drama': 90,\n",
              " 'thriller': 234,\n",
              " 'action crime': 4,\n",
              " 'crime drama': 68,\n",
              " 'drama thriller': 106,\n",
              " 'adventure science': 29,\n",
              " 'animation': 33,\n",
              " 'family': 109,\n",
              " 'animation family': 38,\n",
              " 'fantasy family': 130,\n",
              " 'action science': 12,\n",
              " 'adventure action': 17,\n",
              " 'action thriller': 13,\n",
              " 'thriller crime': 238,\n",
              " 'western': 265,\n",
              " 'adventure western': 32,\n",
              " 'adventure family': 23,\n",
              " 'family fantasy': 115,\n",
              " 'fiction action': 139,\n",
              " 'action fantasy': 7,\n",
              " 'comedy': 44,\n",
              " 'action comedy': 3,\n",
              " 'comedy science': 59,\n",
              " 'adventure drama': 22,\n",
              " 'drama action': 91,\n",
              " 'romance': 214,\n",
              " 'drama romance': 104,\n",
              " 'romance thriller': 228,\n",
              " 'thriller action': 235,\n",
              " 'fiction thriller': 150,\n",
              " 'adventure thriller': 30,\n",
              " 'fantasy adventure': 126,\n",
              " 'family adventure': 111,\n",
              " 'adventure comedy': 19,\n",
              " 'thriller science': 247,\n",
              " 'family comedy': 113,\n",
              " 'horror': 170,\n",
              " 'action drama': 5,\n",
              " 'drama horror': 101,\n",
              " 'horror science': 180,\n",
              " 'action family': 6,\n",
              " 'action romance': 11,\n",
              " 'fiction adventure': 140,\n",
              " 'adventure animation': 18,\n",
              " 'animation adventure': 35,\n",
              " 'comedy family': 51,\n",
              " 'family action': 110,\n",
              " 'comedy crime': 48,\n",
              " 'crime thriller': 78,\n",
              " 'animation drama': 37,\n",
              " 'fiction fantasy': 146,\n",
              " 'animation comedy': 36,\n",
              " 'drama family': 97,\n",
              " 'fiction western': 152,\n",
              " 'crime fantasy': 70,\n",
              " 'fantasy comedy': 128,\n",
              " 'drama comedy': 94,\n",
              " 'comedy animation': 47,\n",
              " 'drama fantasy': 98,\n",
              " 'fantasy drama': 129,\n",
              " 'family romance': 120,\n",
              " 'fiction romance': 149,\n",
              " 'comedy adventure': 46,\n",
              " 'fantasy animation': 127,\n",
              " 'mystery': 200,\n",
              " 'family mystery': 119,\n",
              " 'mystery science': 210,\n",
              " 'family animation': 112,\n",
              " 'animation action': 34,\n",
              " 'drama science': 105,\n",
              " 'fiction mystery': 148,\n",
              " 'mystery adventure': 202,\n",
              " 'thriller mystery': 245,\n",
              " 'mystery romance': 209,\n",
              " 'comedy drama': 50,\n",
              " 'thriller adventure': 236,\n",
              " 'history': 158,\n",
              " 'war': 252,\n",
              " 'history romance': 166,\n",
              " 'romance war': 230,\n",
              " 'war history': 258,\n",
              " 'history action': 159,\n",
              " 'horror action': 171,\n",
              " 'comedy fantasy': 52,\n",
              " 'horror thriller': 181,\n",
              " 'drama war': 107,\n",
              " 'action animation': 2,\n",
              " 'action war': 14,\n",
              " 'action mystery': 10,\n",
              " 'drama mystery': 103,\n",
              " 'animation fantasy': 39,\n",
              " 'western drama': 271,\n",
              " 'drama adventure': 92,\n",
              " 'family western': 123,\n",
              " 'western adventure': 267,\n",
              " 'mystery thriller': 211,\n",
              " 'animation science': 42,\n",
              " 'fiction family': 145,\n",
              " 'comedy thriller': 60,\n",
              " 'animation mystery': 41,\n",
              " 'family science': 121,\n",
              " 'crime mystery': 75,\n",
              " 'fantasy horror': 131,\n",
              " 'adventure mystery': 27,\n",
              " 'action history': 8,\n",
              " 'history war': 168,\n",
              " 'thriller drama': 240,\n",
              " 'drama history': 100,\n",
              " 'war action': 253,\n",
              " 'crime comedy': 67,\n",
              " 'fiction drama': 144,\n",
              " 'adventure history': 25,\n",
              " 'music': 187,\n",
              " 'romance family': 221,\n",
              " 'animation music': 40,\n",
              " 'comedy romance': 58,\n",
              " 'history crime': 160,\n",
              " 'drama crime': 95,\n",
              " 'drama western': 108,\n",
              " 'drama animation': 93,\n",
              " 'thriller romance': 246,\n",
              " 'horror mystery': 178,\n",
              " 'romance fantasy': 222,\n",
              " 'family drama': 114,\n",
              " 'western history': 272,\n",
              " 'mystery drama': 204,\n",
              " 'thriller history': 242,\n",
              " 'comedy action': 45,\n",
              " 'thriller fantasy': 241,\n",
              " 'fantasy thriller': 136,\n",
              " 'romance science': 227,\n",
              " 'history thriller': 167,\n",
              " 'romance drama': 220,\n",
              " 'fantasy romance': 134,\n",
              " 'drama music': 102,\n",
              " 'horror comedy': 172,\n",
              " 'family music': 118,\n",
              " 'music animation': 189,\n",
              " 'crime science': 77,\n",
              " 'romance comedy': 218,\n",
              " 'fantasy mystery': 133,\n",
              " 'mystery family': 205,\n",
              " 'war drama': 257,\n",
              " 'western animation': 268,\n",
              " 'fiction war': 151,\n",
              " 'music romance': 197,\n",
              " 'documentary': 80,\n",
              " 'documentary family': 85,\n",
              " 'thriller horror': 243,\n",
              " 'comedy music': 56,\n",
              " 'crime romance': 76,\n",
              " 'war science': 262,\n",
              " 'thriller war': 248,\n",
              " 'fantasy music': 132,\n",
              " 'fantasy war': 137,\n",
              " 'action horror': 9,\n",
              " 'music family': 194,\n",
              " 'mystery action': 201,\n",
              " 'history drama': 162,\n",
              " 'crime action': 65,\n",
              " 'action western': 15,\n",
              " 'mystery western': 213,\n",
              " 'adventure war': 31,\n",
              " 'music documentary': 192,\n",
              " 'fiction horror': 147,\n",
              " 'fiction animation': 141,\n",
              " 'romance adventure': 216,\n",
              " 'adventure horror': 26,\n",
              " 'fiction comedy': 142,\n",
              " 'comedy western': 63,\n",
              " 'family thriller': 122,\n",
              " 'crime adventure': 66,\n",
              " 'crime family': 69,\n",
              " 'horror romance': 179,\n",
              " 'romance animation': 217,\n",
              " 'comedy horror': 55,\n",
              " 'horror fantasy': 176,\n",
              " 'adventure romance': 28,\n",
              " 'music drama': 193,\n",
              " 'mystery war': 212,\n",
              " 'romance western': 231,\n",
              " 'history western': 169,\n",
              " 'western thriller': 275,\n",
              " 'romance action': 215,\n",
              " 'romance horror': 225,\n",
              " 'history mystery': 165,\n",
              " 'comedy war': 62,\n",
              " 'crime history': 72,\n",
              " 'war adventure': 254,\n",
              " 'war romance': 261,\n",
              " 'western crime': 270,\n",
              " 'crime horror': 73,\n",
              " 'war crime': 256,\n",
              " 'music comedy': 190,\n",
              " 'romance history': 224,\n",
              " 'mystery fantasy': 206,\n",
              " 'romance crime': 219,\n",
              " 'family horror': 117,\n",
              " 'thriller music': 244,\n",
              " 'war music': 259,\n",
              " 'mystery crime': 203,\n",
              " 'comedy documentary': 49,\n",
              " 'documentary action': 81,\n",
              " 'horror drama': 175,\n",
              " 'music adventure': 188,\n",
              " 'western music': 273,\n",
              " 'romance music': 226,\n",
              " 'foreign': 153,\n",
              " 'drama foreign': 99,\n",
              " 'thriller western': 249,\n",
              " 'crime war': 79,\n",
              " 'mystery horror': 207,\n",
              " 'music crime': 191,\n",
              " 'history music': 164,\n",
              " 'war comedy': 255,\n",
              " 'western action': 266,\n",
              " 'horror crime': 173,\n",
              " 'history family': 163,\n",
              " 'thriller comedy': 237,\n",
              " 'documentary music': 88,\n",
              " 'crime music': 74,\n",
              " 'comedy mystery': 57,\n",
              " 'crime foreign': 71,\n",
              " 'romance foreign': 223,\n",
              " 'western romance': 274,\n",
              " 'mystery music': 208,\n",
              " 'foreign romance': 156,\n",
              " 'foreign history': 155,\n",
              " 'family foreign': 116,\n",
              " 'music science': 198,\n",
              " 'tv': 250,\n",
              " 'movie': 182,\n",
              " 'tv movie': 251,\n",
              " 'movie romance': 186,\n",
              " 'music mystery': 196,\n",
              " 'fiction crime': 143,\n",
              " 'war mystery': 260,\n",
              " 'comedy foreign': 53,\n",
              " 'documentary comedy': 82,\n",
              " 'war thriller': 263,\n",
              " 'movie crime': 184,\n",
              " 'music tv': 199,\n",
              " 'drama documentary': 96,\n",
              " 'comedy history': 54,\n",
              " 'romance tv': 229,\n",
              " 'war western': 264,\n",
              " 'movie horror': 185,\n",
              " 'movie action': 183,\n",
              " 'western comedy': 269,\n",
              " 'documentary drama': 84,\n",
              " 'animation war': 43,\n",
              " 'history documentary': 161,\n",
              " 'horror history': 177,\n",
              " 'thriller documentary': 239,\n",
              " 'documentary crime': 83,\n",
              " 'horror documentary': 174,\n",
              " 'documentary mystery': 89,\n",
              " 'adventure documentary': 21,\n",
              " 'documentary foreign': 86,\n",
              " 'music horror': 195,\n",
              " 'foreign documentary': 154,\n",
              " 'comedy tv': 61,\n",
              " 'documentary history': 87,\n",
              " 'foreign thriller': 157}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genre_matrix = countVect.fit_transform(df_movies['genres_literal'])\n",
        "genre_matrix.shape\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "genre_similarity = cosine_similarity(genre_matrix, genre_matrix)\n",
        "genre_similarity[:3]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pg0EFpoA8Xu_",
        "outputId": "949c75ee-709b-488c-8a0f-a75614b64ac8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.59628479, 0.4472136 , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.59628479, 1.        , 0.4       , ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.4472136 , 0.4       , 1.        , ..., 0.        , 0.        ,\n",
              "        0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genre_similarity_sorted_idx = genre_similarity.argsort()[:,::-1]\n",
        "genre_similarity_sorted_idx[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bDFc_9UT8exc",
        "outputId": "7709e7b7-900c-4ef8-e148-7ee2dd1d2956"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   0,   46,  813, ..., 3037, 3036, 2399],\n",
              "       [ 329,  129,    1, ..., 3066, 3065, 2399],\n",
              "       [1542,    2, 1740, ..., 2998, 2997, 2399]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_similarity_movie(dataFrame, similarity_sorted_idx, movieName, top=10):\n",
        "  title_movie = dataFrame[dataFrame['title'].str.lower().isin([movieName.lower()])]\n",
        "  title_idx = title_movie.index.values\n",
        "  similar_indexes = similarity_sorted_idx[title_idx, :top]\n",
        "  print(similar_indexes.ndim, '차원 : ', similar_indexes)\n",
        "  similar_indexes = similar_indexes.reshape(-1)\n",
        "  print(similar_indexes.ndim, '차원 : ', similar_indexes)\n",
        "  \n",
        "  return dataFrame.iloc[similar_indexes]\n",
        "\n",
        "find_similarity_movie(df_movies, genre_similarity_sorted_idx, 'avatar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "3XRP2P3A8k3_",
        "outputId": "60af453d-be23-4996-ab38-8321a57dc376"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 차원 :  [[   0   46  813  870   14 3493 1296 1652  419  420]]\n",
            "1 차원 :  [   0   46  813  870   14 3493 1296 1652  419  420]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id                                      title  \\\n",
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              "1652   14164                       Dragonball Evolution   \n",
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              "420    11253                Hellboy II: The Golden Army   \n",
              "\n",
              "                                                 genres  vote_average  \\\n",
              "0         [Action, Adventure, Fantasy, Science Fiction]           7.2   \n",
              "46        [Action, Adventure, Fantasy, Science Fiction]           7.5   \n",
              "813       [Action, Adventure, Fantasy, Science Fiction]           6.9   \n",
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              "14        [Action, Adventure, Fantasy, Science Fiction]           6.5   \n",
              "3494      [Action, Adventure, Fantasy, Science Fiction]           4.6   \n",
              "1296  [Comedy, Action, Adventure, Fantasy, Science F...           5.3   \n",
              "1652  [Action, Adventure, Fantasy, Science Fiction, ...           2.9   \n",
              "419               [Adventure, Fantasy, Science Fiction]           5.9   \n",
              "420               [Adventure, Fantasy, Science Fiction]           6.5   \n",
              "\n",
              "      vote_count  popularity  \\\n",
              "0          11800  150.437577   \n",
              "46          6032  118.078691   \n",
              "813         1022   48.507081   \n",
              "870          629   30.515175   \n",
              "14          6359   99.398009   \n",
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              "419         1799   21.218000   \n",
              "420         1527   58.579760   \n",
              "\n",
              "                                               keywords  \\\n",
              "0     [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
              "46    [{\"id\": 1228, \"name\": \"1970s\"}, {\"id\": 1852, \"...   \n",
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              "870   [{\"id\": 83, \"name\": \"saving the world\"}, {\"id\"...   \n",
              "14    [{\"id\": 83, \"name\": \"saving the world\"}, {\"id\"...   \n",
              "3494  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...   \n",
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              "1652  [{\"id\": 3436, \"name\": \"karate\"}, {\"id\": 9715, ...   \n",
              "419   [{\"id\": 704, \"name\": \"adolescence\"}, {\"id\": 81...   \n",
              "420   [{\"id\": 2096, \"name\": \"auction\"}, {\"id\": 7005,...   \n",
              "\n",
              "                                               overview  \\\n",
              "0     In the 22nd century, a paraplegic Marine is di...   \n",
              "46    The ultimate X-Men ensemble fights a war for t...   \n",
              "813   Mild-mannered Clark Kent works as a reporter a...   \n",
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              "14    A young boy learns that he has extraordinary p...   \n",
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              "1296  Aiming to defeat the Man of Steel, wealthy exe...   \n",
              "1652  The young warrior Son Goku sets out on a quest...   \n",
              "419   David Rice is a man who knows no boundaries, a...   \n",
              "420   In this continuation to the adventure of the d...   \n",
              "\n",
              "                                         genres_literal  \n",
              "0              Action Adventure Fantasy Science Fiction  \n",
              "46             Action Adventure Fantasy Science Fiction  \n",
              "813            Action Adventure Fantasy Science Fiction  \n",
              "870            Action Adventure Fantasy Science Fiction  \n",
              "14             Action Adventure Fantasy Science Fiction  \n",
              "3494           Action Adventure Fantasy Science Fiction  \n",
              "1296    Comedy Action Adventure Fantasy Science Fiction  \n",
              "1652  Action Adventure Fantasy Science Fiction Thriller  \n",
              "419                   Adventure Fantasy Science Fiction  \n",
              "420                   Adventure Fantasy Science Fiction  "
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    {
      "cell_type": "code",
      "source": [
        "movies_top10 = find_similarity_movie(df_movies, genre_similarity_sorted_idx, 'avatar')\n",
        "movies_top10[['title', 'vote_average']]"
      ],
      "metadata": {
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      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 차원 :  [[   0   46  813  870   14 3493 1296 1652  419  420]]\n",
            "1 차원 :  [   0   46  813  870   14 3493 1296 1652  419  420]\n"
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              "                                          title  vote_average\n",
              "0                                        Avatar           7.2\n",
              "46                   X-Men: Days of Future Past           7.5\n",
              "813                                    Superman           6.9\n",
              "870                                 Superman II           6.5\n",
              "14                                 Man of Steel           6.5\n",
              "3494  Beastmaster 2: Through the Portal of Time           4.6\n",
              "1296                               Superman III           5.3\n",
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              "      background-color: #434B5C;\n",
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              "      <script>\n",
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              "          document.querySelector('#df-a5e692de-62cf-40c9-b6d3-a0e0d3d942ad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5e692de-62cf-40c9-b6d3-a0e0d3d942ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies[['title', 'vote_average', 'vote_count']]\n",
        "df_movies[['title', 'vote_average', 'vote_count']].sort_values('vote_average', ascending=False)[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OdGo4QL9_Xrz",
        "outputId": "c4d6de94-e25b-4cc4-a226-3ef7d79563c0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         title  vote_average  vote_count\n",
              "4247     Me You and Five Bucks          10.0           2\n",
              "4662            Little Big Top          10.0           1\n",
              "4045     Dancer, Texas Pop. 81          10.0           1\n",
              "3519          Stiff Upper Lips          10.0           1\n",
              "3992                 Sardaarji           9.5           2\n",
              "2386            One Man's Hero           9.3           2\n",
              "1881  The Shawshank Redemption           8.5        8205\n",
              "2970        There Goes My Baby           8.5           2\n",
              "2796     The Prisoner of Zenda           8.4          11\n",
              "3337             The Godfather           8.4        5893"
            ],
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              "      <th>4662</th>\n",
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              "    <tr>\n",
              "      <th>4045</th>\n",
              "      <td>Dancer, Texas Pop. 81</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3519</th>\n",
              "      <td>Stiff Upper Lips</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>Sardaarji</td>\n",
              "      <td>9.5</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2386</th>\n",
              "      <td>One Man's Hero</td>\n",
              "      <td>9.3</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1881</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2970</th>\n",
              "      <td>There Goes My Baby</td>\n",
              "      <td>8.5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2796</th>\n",
              "      <td>The Prisoner of Zenda</td>\n",
              "      <td>8.4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3337</th>\n",
              "      <td>The Godfather</td>\n",
              "      <td>8.4</td>\n",
              "      <td>5893</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
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    {
      "cell_type": "code",
      "source": [
        "# 전체 영화에 대한 평균 평점\n",
        "df_movies['vote_average']\n",
        "C = df_movies['vote_average'].mean()"
      ],
      "metadata": {
        "id": "Hw8ZuOvFA7FQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# m : 평점을 부여하기 위한 최소 투표 횟수\n",
        "m = df_movies['vote_count'].quantile(0.6)"
      ],
      "metadata": {
        "id": "dnf1MAhSBEgj"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 가중 평점(Weighted Rating) = (v/v(v+m)) * R + (m/(v+m)) * C\n",
        "# v : 개별 영화에 평점을 투표한 횟수\n",
        "v = df_movies['vote_count']"
      ],
      "metadata": {
        "id": "HSy-cWvnB9_z"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# R : 개별 영화에 대한 평균 평점\n",
        "R = df_movies['vote_average']"
      ],
      "metadata": {
        "id": "wRu3ZkS3COjH"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_weighted_vote_average(df_movies):\n",
        "  v = df_movies['vote_count']\n",
        "  m = df_movies['vote_count'].quantile(0.6)\n",
        "  R = df_movies['vote_average']\n",
        "  C = df_movies['vote_average'].mean()\n",
        "  \n",
        "  return (v/(v+m)) * R + (m/(v+m)) * C"
      ],
      "metadata": {
        "id": "vNgKZOAeGZbf"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies['weighted_vote_average'] = get_weighted_vote_average(df_movies)\n",
        "df_movies[['title', 'vote_average', 'vote_count', 'weighted_vote_average']][:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "k7-hBFTuGxey",
        "outputId": "67ee8990-3440-4792-ad4b-cbb373f08116"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      title  vote_average  vote_count  \\\n",
              "0                                    Avatar           7.2       11800   \n",
              "1  Pirates of the Caribbean: At World's End           6.9        4500   \n",
              "2                                   Spectre           6.3        4466   \n",
              "\n",
              "   weighted_vote_average  \n",
              "0               7.166254  \n",
              "1               6.838528  \n",
              "2               6.284117  "
            ],
            "text/html": [
              "\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Avatar</td>\n",
              "      <td>7.2</td>\n",
              "      <td>11800</td>\n",
              "      <td>7.166254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>6.9</td>\n",
              "      <td>4500</td>\n",
              "      <td>6.838528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spectre</td>\n",
              "      <td>6.3</td>\n",
              "      <td>4466</td>\n",
              "      <td>6.284117</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_similarity_movie(dataFrame, similarity_sorted_idx, movieName, top=10):\n",
        "  title_movie = dataFrame[dataFrame['title'].str.lower().isin([movieName.lower()])]\n",
        "  title_idx = title_movie.index.values\n",
        "  similar_indexes = similarity_sorted_idx[title_idx, :top+5]\n",
        "  # print(similar_indexes.ndim, '차원 : ', similar_indexes)\n",
        "  similar_indexes = similar_indexes.reshape(-1)\n",
        "  # print(similar_indexes.ndim, '차원 : ', similar_indexes)\n",
        "  \n",
        "  similar_indexes = similar_indexes[similar_indexes!=title_idx]\n",
        "\n",
        "  return dataFrame.iloc[similar_indexes].sort_values('weighted_vote_average', ascending=False)[:top]\n",
        "\n",
        "find_movies = find_similarity_movie(df_movies, genre_similarity_sorted_idx, 'avatar')\n",
        "lst = ['title', 'vote_average', 'vote_count', 'weighted_vote_average']\n",
        "find_movies[lst]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "nqjh16EIHowD",
        "outputId": "8f44b3af-6e9b-4d5b-f58a-60996532a6de"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          title  vote_average  vote_count  \\\n",
              "46                   X-Men: Days of Future Past           7.5        6032   \n",
              "813                                    Superman           6.9        1022   \n",
              "14                                 Man of Steel           6.5        6359   \n",
              "420                 Hellboy II: The Golden Army           6.5        1527   \n",
              "870                                 Superman II           6.5         629   \n",
              "3208            Star Wars: Clone Wars: Volume 1           8.0          27   \n",
              "1191                             Small Soldiers           6.2         511   \n",
              "1932                                     Sheena           5.0          22   \n",
              "3494  Beastmaster 2: Through the Portal of Time           4.6          17   \n",
              "419                                      Jumper           5.9        1799   \n",
              "\n",
              "      weighted_vote_average  \n",
              "46                 7.418471  \n",
              "813                6.685048  \n",
              "14                 6.477559  \n",
              "420                6.420428  \n",
              "870                6.348972  \n",
              "3208               6.222292  \n",
              "1191               6.154957  \n",
              "1932               6.031736  \n",
              "3494               6.027505  \n",
              "419                5.932983  "
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    },
    {
      "cell_type": "code",
      "source": [
        "movies = [\n",
        "  ['user1', '연평대전', 5],\n",
        "  ['user1', '7번방의 선물', 4],\n",
        "  ['user1', '국제시장', 4],\n",
        "  ['user2', '연평대전', 5],\n",
        "  ['user2', '7번방의 선물', 3],\n",
        "  ['user2', '국제시장', 4],\n",
        "  ['user2', '명랑', 5],\n",
        "  ['user2', '국가대표', 3],\n",
        "  # ['user3', '연평대전', 4],\n",
        "  ['user3', '7번방의 선물', 3],\n",
        "  # ['user3', '국제시장', 3],\n",
        "  ['user3', '명랑', 3],\n",
        "  ['user3', '국가대표', 5]\n",
        "]"
      ],
      "metadata": {
        "id": "mdcDMgbcVcU8"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_movies = pd.DataFrame(movies, columns=['userID', 'movieName', 'Rating'])\n",
        "df_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "DsPRoWRcVgC4",
        "outputId": "2e665f90-1652-47b3-c3d8-30a3902eba77"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userID movieName  Rating\n",
              "0   user1      연평대전       5\n",
              "1   user1   7번방의 선물       4\n",
              "2   user1      국제시장       4\n",
              "3   user2      연평대전       5\n",
              "4   user2   7번방의 선물       3\n",
              "5   user2      국제시장       4\n",
              "6   user2        명랑       5\n",
              "7   user2      국가대표       3\n",
              "8   user3   7번방의 선물       3\n",
              "9   user3        명랑       3\n",
              "10  user3      국가대표       5"
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# df_movies.pivot_table(데이터, row, col)\n",
        "df_user_movies = df_movies.pivot_table('Rating', index='userID', columns='movieName')\n",
        "df_user_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Wv2BKj68WQ5I",
        "outputId": "28a8cf8f-e5dc-493a-bd51-c2349bfe9a12"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieName  7번방의 선물  국가대표  국제시장   명랑  연평대전\n",
              "userID                                   \n",
              "user1          4.0   NaN   4.0  NaN   5.0\n",
              "user2          3.0   3.0   4.0  5.0   5.0\n",
              "user3          3.0   5.0   NaN  3.0   NaN"
            ],
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              "\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_movies = df_user_movies.fillna(0)\n",
        "df_user_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1RFINl1NXNhE",
        "outputId": "7117d4ab-32fc-4bb6-ebbc-5c47b1527d7b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieName  7번방의 선물  국가대표  국제시장   명랑  연평대전\n",
              "userID                                   \n",
              "user1          4.0   0.0   4.0  0.0   5.0\n",
              "user2          3.0   3.0   4.0  5.0   5.0\n",
              "user3          3.0   5.0   0.0  3.0   0.0"
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 유클리디안 유사도\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "\n",
        "euclidean_distances(df_user_movies, df_user_movies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c216ji5JXwVU",
        "outputId": "5279848c-7f1a-459b-8d6f-1263aa7d071f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 5.91607978, 8.71779789],\n",
              "       [5.91607978, 0.        , 7.        ],\n",
              "       [8.71779789, 7.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 코사인 유사도\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "usr_sim = cosine_similarity(df_user_movies, df_user_movies)\n",
        "usr_sim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UG3YRxpYXyUG",
        "outputId": "2b6d3ff9-1831-437c-f6d3-9aeb1a8a7479"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.76594714, 0.24238715],\n",
              "       [0.76594714, 1.        , 0.64891937],\n",
              "       [0.24238715, 0.64891937, 1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "usr_based_collab = pd.DataFrame(usr_sim, index=df_user_movies.index, columns=df_user_movies.index)\n",
        "usr_based_collab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QJzJ43JjbLcf",
        "outputId": "96519f8d-30f7-4853-c09c-0b5fe9c7d3a0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userID     user1     user2     user3\n",
              "userID                              \n",
              "user1   1.000000  0.765947  0.242387\n",
              "user2   0.765947  1.000000  0.648919\n",
              "user3   0.242387  0.648919  1.000000"
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "usr_based_collab['user1'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3Zbgu0tbXbs",
        "outputId": "12970733-d654-4c4f-bc20-a844c035a833"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userID\n",
              "user1    1.000000\n",
              "user2    0.765947\n",
              "user3    0.242387\n",
              "Name: user1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# user2가 본 영화 추천\n",
        "df_user_movies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1vHm1oyqiMpH",
        "outputId": "c3506a3f-1df2-4dc2-c8a1-469b0a3a70fa"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieName  7번방의 선물  국가대표  국제시장   명랑  연평대전\n",
              "userID                                   \n",
              "user1          4.0   0.0   4.0  0.0   5.0\n",
              "user2          3.0   3.0   4.0  5.0   5.0\n",
              "user3          3.0   5.0   0.0  3.0   0.0"
            ],
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              "\n",
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              "      <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>movieName</th>\n",
              "      <th>7번방의 선물</th>\n",
              "      <th>국가대표</th>\n",
              "      <th>국제시장</th>\n",
              "      <th>명랑</th>\n",
              "      <th>연평대전</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>userID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user1</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fddf3a1a-1b71-447b-9b94-e450617d422a')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fddf3a1a-1b71-447b-9b94-e450617d422a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_movies.loc['user2'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WKI9I24lV82",
        "outputId": "1dbbe725-2810-468e-fc2f-1a29097ed3ac"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieName\n",
              "명랑         5.0\n",
              "연평대전       5.0\n",
              "국제시장       4.0\n",
              "7번방의 선물    3.0\n",
              "국가대표       3.0\n",
              "Name: user2, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전치\n",
        "df_user_movies_T = df_user_movies.transpose()\n",
        "\n",
        "df_user_movies_T[df_user_movies_T['user1']==0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "nT50FT8Ml8UZ",
        "outputId": "dc56fbce-0891-41d0-e544-47ce504d8829"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userID     user1  user2  user3\n",
              "movieName                     \n",
              "국가대표         0.0    3.0    5.0\n",
              "명랑           0.0    5.0    3.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fbbb8032-7ee2-41fa-9f32-026c290fba23\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>userID</th>\n",
              "      <th>user1</th>\n",
              "      <th>user2</th>\n",
              "      <th>user3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieName</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>국가대표</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>명랑</th>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbbb8032-7ee2-41fa-9f32-026c290fba23')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fbbb8032-7ee2-41fa-9f32-026c290fba23 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fbbb8032-7ee2-41fa-9f32-026c290fba23');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user=df_user_movies_T[df_user_movies_T['user1']==0]\n",
        "df_user['user2'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9EU9diimwzD",
        "outputId": "6db21b2f-e194-4a26-9004-748109a2a133"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movieName\n",
              "명랑      5.0\n",
              "국가대표    3.0\n",
              "Name: user2, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 아이템 기반 추천 알고리즘\n",
        "df_user_movies_T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "9tPcEU3upXQY",
        "outputId": "c15dbf3c-fcca-4d25-9192-5b5179df911f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "userID     user1  user2  user3\n",
              "movieName                     \n",
              "7번방의 선물      4.0    3.0    3.0\n",
              "국가대표         0.0    3.0    5.0\n",
              "국제시장         4.0    4.0    0.0\n",
              "명랑           0.0    5.0    3.0\n",
              "연평대전         5.0    5.0    0.0"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>userID</th>\n",
              "      <th>user1</th>\n",
              "      <th>user2</th>\n",
              "      <th>user3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>movieName</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7번방의 선물</th>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>국가대표</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>국제시장</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>명랑</th>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>연평대전</th>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-241d19f5-6a92-4298-90ca-7ab7b84203aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "item_based_sim = cosine_similarity(df_user_movies_T, df_user_movies_T)\n",
        "item_based_sim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RoAb58mQ_cbQ",
        "outputId": "5d21fb8d-1f41-4ef8-a59d-03eb06a103b0"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.70588235, 0.84887469, 0.70588235, 0.84887469],\n",
              "       [0.70588235, 1.        , 0.36380344, 0.88235294, 0.36380344],\n",
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              "       [0.84887469, 0.36380344, 1.        , 0.60633906, 1.        ]])"
            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# item_based_sim.dot(df_user_movies)\n",
        "df_user_movies.dot(item_based_sim)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
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        "id": "JNKNsku7AEKG",
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      "execution_count": 36,
      "outputs": [
        {
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              "                0          1          2          3          4\n",
              "userID                                                       \n",
              "user1   11.639872   6.097760  12.395499   8.280581  12.395499\n",
              "user2   16.286931  12.803643  15.669730  15.221757  15.669730\n",
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        "np.abs(item_based_sim)\n",
        "np.abs(item_based_sim).sum(axis=1)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4.10951408, 3.31584217, 3.81901719, 3.80091342, 3.81901719])"
            ]
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          "metadata": {},
          "execution_count": 37
        }
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    {
      "cell_type": "code",
      "source": [
        "df_pred_movies=df_user_movies.dot(item_based_sim)/np.abs(item_based_sim).sum(axis=1)\n",
        "df_pred_movies"
      ],
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        "colab": {
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          "height": 175
        },
        "id": "3FC9BhI9AprW",
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      },
      "execution_count": 38,
      "outputs": [
        {
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              "               0         1         2         3         4\n",
              "userID                                                  \n",
              "user1   2.832421  1.838978  3.245730  2.178577  3.245730\n",
              "user2   3.963226  3.861355  4.103079  4.004763  4.103079\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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        "df_pred_movies.values-df_user_movies.values"
      ],
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        "id": "7YsnWRdoB8HC",
        "outputId": "8b039914-48ae-462e-dd22-aacb62ffe138"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.16757944,  1.83897786, -0.75426998,  2.17857658, -1.75426998],\n",
              "       [ 0.9632255 ,  0.86135469,  0.10307912, -0.995237  , -0.89692088],\n",
              "       [-0.89584397, -2.0551355 ,  1.61943718, -0.49286271,  1.61943718]])"
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          "metadata": {},
          "execution_count": 39
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    },
    {
      "cell_type": "code",
      "source": [
        "rating_square = np.square(df_pred_movies.values-df_user_movies.values)\n",
        "rating_square"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IX9yK013CCJC",
        "outputId": "83016000-1191-48a1-e1a5-41fc977980bd"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.36324174, 3.38183958, 0.5689232 , 4.74619593, 3.07746315],\n",
              "       [0.92780337, 0.7419319 , 0.01062531, 0.99049668, 0.80446706],\n",
              "       [0.80253642, 4.22358194, 2.62257679, 0.24291365, 2.62257679]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_square.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4YfrrKbCPVg",
        "outputId": "4a9252fb-78ee-487f-81d4-37011456dd40"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8084782338349181"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%timeit rating_square.reshape(-1).mean()\n",
        "%timeit rating_square.flatten().mean()\n",
        "%timeit rating_square.ravel().mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brsjdHAxDfyT",
        "outputId": "ec01aa56-a3e0-4f4d-d36f-0bb2212258b9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7.06 µs ± 271 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
            "7.66 µs ± 436 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
            "6 µs ± 137 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_movies.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dbqhDMWE_BP",
        "outputId": "aabe86a4-1496-46fe-b662-6669a1693df5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4., 0., 4., 0., 5.],\n",
              "       [3., 3., 4., 5., 5.],\n",
              "       [3., 5., 0., 3., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_user_movies.values.nonzero()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNH-dBsuFDWS",
        "outputId": "7796dc38-a0b3-4bea-96ee-5ce5e93da4c0"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2]),\n",
              " array([0, 2, 4, 0, 1, 2, 3, 4, 0, 1, 3]))"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_movies_nonzero = df_user_movies.values[df_user_movies.values.nonzero()]"
      ],
      "metadata": {
        "id": "GUYLpjq8FM_9"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_movies_nonzero = df_pred_movies.values[df_user_movies.values.nonzero()]"
      ],
      "metadata": {
        "id": "kkr1U9ZDFeeL"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.square(user_movies_nonzero-pred_movies_nonzero).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtYr534PF2TM",
        "outputId": "4c6e8a1b-e15e-4a62-f872-fb96cc269f8c"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.250362218706176"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_arr=df_user_movies.values\n",
        "ratings_arr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cBYs0YtLr5s",
        "outputId": "08f68533-00e3-4302-f4fe-3da6a027cdc6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4., 0., 4., 0., 5.],\n",
              "       [3., 3., 4., 5., 5.],\n",
              "       [3., 5., 0., 3., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_arr[0, :]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGRNAYr8MUI0",
        "outputId": "b896a904-3a6e-42d4-e1a9-bae0793fa7c8"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4., 0., 4., 0., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_arg = np.argsort(ratings_arr[0, :])\n",
        "ratings_arg "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5-s5Y7JMboC",
        "outputId": "81f1b583-8ff2-4149-c1ab-57adf1ac9b43"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 0, 2, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_arg[:4]\n",
        "ratings_arg[:-1]\n",
        "ratings_arg[:-4]\n",
        "ratings_arg[:-4:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vhpivm8aMvqc",
        "outputId": "499037c3-dc25-43c5-c471-8ee9d2687025"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n=3\n",
        "top_n_arg = np.argsort(ratings_arr[0,:])[:-1-n:-1]"
      ],
      "metadata": {
        "id": "N4nOtecDOTVI"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_arr[0][top_n_arg]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWEXHOZLOeBW",
        "outputId": "157fdb4b-e546-4fe7-9588-ea3665d33c44"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5., 4., 4.])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}