241104_과제_추천시스템실습.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ggoddll99/24-2ESAA/blob/main/241104_%EA%B3%BC%EC%A0%9C_%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C%EC%8B%A4%EC%8A%B5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **05. 콘텐츠 기반 필터링 실습 - TMDB 5000 영화 데이터 세트**"
      ],
      "metadata": {
        "id": "BJVPDEXMadFa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TMDB 5000 영화 데이터 세트는 유명한 영화 데이터 정보 사이트인 IMDB의 많은 영화 중 주요 5000개 영화에 대한 메타 정보를 새롭게 가공해 캐글에서 제공하는 데이터 세트이다."
      ],
      "metadata": {
        "id": "NXAm3aRQaipb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **장르 속성을 이용한 영화 콘텐츠 기반 필터링**\n",
        "콘텐츠 기반 필터링은 사용자가 특정 영화를 감상하고 그 영화를 좋아했다면 그 영화와 비슷한 특성/속성, 구성 요소 등을 가진 다른 영화를 추천하는 것이다.\n",
        "\n",
        "가령 영화 '인셉션'을 재미있게 봤다면 '인셉션'의 장르인 액션, 공상과학으로 높은 평점을 받은 다른 영화를 추천하거나 '인셉션'의 감독인 크리스토퍼 놀란의 다른 영화를 추천하는 방식이다.\n",
        "\n",
        "이렇게 영화(또는 상품/서비스) 간의 유사성을 판단하는 기준이 영화를 구성하는 다양한 콘텐츠(장르, 감독, 배우, 평점, 키워드, 영화 설명)를 기반으로 하는 방식이 콘텐츠 기반 필터링이다.\n",
        "\n",
        "콘텐츠 기반 필터링 추천 시스템을 영화를 선택하는 데 중요한 요소인 영화 장르 속성을 기반으로 만들어 보자. 장르 칼럼 값의 유사도를 비교한 뒤 그중 높은 평점을 가지는 영화를 추천하는 방식이다."
      ],
      "metadata": {
        "id": "AqpyfyeMbNiz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **데이터 로딩 및 가공**\n",
        "장르 속성을 이용해 콘텐츠 기반 필터링을 수행한다."
      ],
      "metadata": {
        "id": "9Si3DEYqcAoi"
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        "outputId": "2c450a17-e5dc-4970-9c1a-71c93f4165f7"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings; warnings.filterwarnings('ignore')\n",
        "\n",
        "movies=pd.read_csv('/content/drive/MyDrive/24-2/ESAA/과제/IMDB영화평/tmdb_5000_movies.csv')\n",
        "print(movies.shape)\n",
        "movies.head(1)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "6q0fOlEhcVNA",
        "outputId": "84e6ad52-2ae5-4c16-8911-2770a79c6cfe"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4803, 20)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                      homepage     id  \\\n",
              "0  http://www.avatarmovie.com/  19995   \n",
              "\n",
              "                                            keywords original_language  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
              "\n",
              "  original_title                                           overview  \\\n",
              "0         Avatar  In the 22nd century, a paraplegic Marine is di...   \n",
              "\n",
              "   popularity                               production_companies  \\\n",
              "0  150.437577  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
              "\n",
              "                                production_countries release_date     revenue  \\\n",
              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
              "\n",
              "   runtime                                   spoken_languages    status  \\\n",
              "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
              "\n",
              "                       tagline   title  vote_average  vote_count  \n",
              "0  Enter the World of Pandora.  Avatar           7.2       11800  "
            ],
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              "variable_name": "movies",
              "summary": "{\n  \"name\": \"movies\",\n  \"rows\": 4803,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40722391,\n        \"min\": 0,\n        \"max\": 380000000,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          439000,\n          68000000,\n          700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1175,\n        \"samples\": [\n          \"[{\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}]\",\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 35, \\\"name\\\": \\\"Comedy\\\"}, {\\\"id\\\": 80, \\\"name\\\": \\\"Crime\\\"}, {\\\"id\\\": 18, \\\"name\\\": \\\"Drama\\\"}]\",\n          \"[{\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}, {\\\"id\\\": 10751, \\\"name\\\": \\\"Family\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homepage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1691,\n        \"samples\": [\n          \"https://www.warnerbros.com/running-scared\",\n          \"http://www.51birchstreet.com/index.php\",\n          \"http://movies2.foxjapan.com/glee/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88694,\n        \"min\": 5,\n        \"max\": 459488,\n        \"num_unique_values\": 4803,\n        \"samples\": [\n          8427,\n          13006,\n          18041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4222,\n        \"samples\": [\n          \"[{\\\"id\\\": 782, \\\"name\\\": \\\"assassin\\\"}, {\\\"id\\\": 1872, \\\"name\\\": \\\"loss of father\\\"}, {\\\"id\\\": 2908, \\\"name\\\": \\\"secret society\\\"}, {\\\"id\\\": 3045, \\\"name\\\": \\\"mission of murder\\\"}, {\\\"id\\\": 9748, \\\"name\\\": \\\"revenge\\\"}]\",\n          \"[{\\\"id\\\": 2987, \\\"name\\\": \\\"gang war\\\"}, {\\\"id\\\": 4942, \\\"name\\\": \\\"victim of murder\\\"}, {\\\"id\\\": 5332, \\\"name\\\": \\\"greed\\\"}, {\\\"id\\\": 6062, \\\"name\\\": \\\"hostility\\\"}, {\\\"id\\\": 156212, \\\"name\\\": \\\"spaghetti western\\\"}]\",\n          \"[{\\\"id\\\": 703, \\\"name\\\": \\\"detective\\\"}, {\\\"id\\\": 1299, \\\"name\\\": \\\"monster\\\"}, {\\\"id\\\": 6101, \\\"name\\\": \\\"engine\\\"}, {\\\"id\\\": 10988, \\\"name\\\": \\\"based on tv series\\\"}, {\\\"id\\\": 15162, \\\"name\\\": \\\"dog\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"xx\",\n          \"ta\",\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4801,\n        \"samples\": [\n          \"I Spy\",\n          \"Love Letters\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overview\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"When the Switchblade, the most sophisticated prototype stealth fighter created yet, is stolen from the U.S. government, one of the United States' top spies, Alex Scott, is called to action. What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n          \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n          \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.816649749537806,\n        \"min\": 0.0,\n        \"max\": 875.581305,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          13.267631,\n          0.010909,\n          5.842299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_companies\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3697,\n        \"samples\": [\n          \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_countries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 469,\n        \"samples\": [\n          \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"1966-10-16\",\n          \"1987-07-31\",\n          \"1993-09-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162857100,\n        \"min\": 0,\n        \"max\": 2787965087,\n        \"num_unique_values\": 3297,\n        \"samples\": [\n          11833696,\n          10462500,\n          17807569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.611934588844207,\n        \"min\": 0.0,\n        \"max\": 338.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          74.0,\n          85.0,\n          170.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spoken_languages\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Released\",\n          \"Post Production\",\n          \"Rumored\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3944,\n        \"samples\": [\n          \"When you're 17, every day is war.\",\n          \"An Unspeakable Horror. A Creative Genius. Captured For Eternity.\",\n          \"May the schwartz be with you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"I Spy\",\n          \"Who's Your Caddy?\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1946121628478925,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          5.1,\n          7.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234,\n        \"min\": 0,\n        \"max\": 13752,\n        \"num_unique_values\": 1609,\n        \"samples\": [\n          7604,\n          3428,\n          225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "영화 제목, 개요, 인기도, 평점, 투표수, 예산, 키워드 등 영화에 대한 다양한 메타 정보를 가지고 있다. 이 중 콘텐츠 기반 필터링 추천 분석에 사용할 주요 칼럼만 추출해 새롭게 DataFrame으로 만든다.\n",
        "\n",
        "추출할 주요 칼럼\n",
        "* id\n",
        "* 영화 제목 title\n",
        "* 영화가 속한 여러 가지 장르 genres\n",
        "* 평균 평점 vote_average\n",
        "* 평점 투표수 vote_count\n",
        "* 영화의 인기 popularity\n",
        "* 영화를 설명하는 주요 키워드 문구 keywords\n",
        "* 영화에 대한 개요 설명 overview"
      ],
      "metadata": {
        "id": "viZRHSjucnaA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df=movies[['id', 'title', 'genres', 'vote_average', 'vote_count', 'popularity',\n",
        "                  'keywords', 'overview']]"
      ],
      "metadata": {
        "id": "qVUz0ysPdR0W"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "'genres', 'keywords' 등과 같은 칼럼을 보면 [{'id' : 28, 'name' : 'Action'}, {'id' : 12, 'name' : 'Adventure'}]와 같이 파이썬 리스트 내부에 여러 개의 딕셔너리가 있는 형태의 문자열로 표기돼 있다. 이는 한꺼번에 여러 개의 값을 표현하기 위한 표기 방식이다.\n",
        "\n",
        "예를 들어 영화 '아바타'의 genres는 'Action'과 'Adventure' 등의 여러 가지 장르로 구성될 수 있기 때문이다. 하지만 이 칼럼이 DataFrame으로 만들어질 때는 단순히 문자열 형태로 로딩되므로 이 칼럼을 가공하지 않고는 필요한 정보를 추출할 수가 없다. 먼저 해당 칼럼이 어떤 형태로 돼 있는지 확인해 보자."
      ],
      "metadata": {
        "id": "WMqDYpP_dZRP"
      }
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      "source": [
        "pd.set_option('max_colwidth', 100)\n",
        "movies_df[['genres', 'keywords']][:1]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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              "                                                                                                genres  \\\n",
              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {...   \n",
              "\n",
              "                                                                                              keywords  \n",
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              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"[{\\\"id\\\": 1463, \\\"name\\\": \\\"culture clash\\\"}, {\\\"id\\\": 2964, \\\"name\\\": \\\"future\\\"}, {\\\"id\\\": 3386, \\\"name\\\": \\\"space war\\\"}, {\\\"id\\\": 3388, \\\"name\\\": \\\"space colony\\\"}, {\\\"id\\\": 3679, \\\"name\\\": \\\"society\\\"}, {\\\"id\\\": 3801, \\\"name\\\": \\\"space travel\\\"}, {\\\"id\\\": 9685, \\\"name\\\": \\\"futuristic\\\"}, {\\\"id\\\": 9840, \\\"name\\\": \\\"romance\\\"}, {\\\"id\\\": 9882, \\\"name\\\": \\\"space\\\"}, {\\\"id\\\": 9951, \\\"name\\\": \\\"alien\\\"}, {\\\"id\\\": 10148, \\\"name\\\": \\\"tribe\\\"}, {\\\"id\\\": 10158, \\\"name\\\": \\\"alien planet\\\"}, {\\\"id\\\": 10987, \\\"name\\\": \\\"cgi\\\"}, {\\\"id\\\": 11399, \\\"name\\\": \\\"marine\\\"}, {\\\"id\\\": 13065, \\\"name\\\": \\\"soldier\\\"}, {\\\"id\\\": 14643, \\\"name\\\": \\\"battle\\\"}, {\\\"id\\\": 14720, \\\"name\\\": \\\"love affair\\\"}, {\\\"id\\\": 165431, \\\"name\\\": \\\"anti war\\\"}, {\\\"id\\\": 193554, \\\"name\\\": \\\"power relations\\\"}, {\\\"id\\\": 206690, \\\"name\\\": \\\"mind and soul\\\"}, {\\\"id\\\": 209714, \\\"name\\\": \\\"3d\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "genres 칼럼은 여러 개의 개별 장르 데이터를 가지고 있고, 이 개별 장르의 명칭은 딕셔너리의 키인 'name'으로 추출할 수 있다. Keywords 역시 마찬가지 구조를 가지고 있다.\n",
        "\n",
        "genres 칼럼의 문자열을 분해해서 개별 장르를 파이썬 리스트 객체로 추출한다.\n",
        "* 파이썬 ast 모듈의 `literal_eval()` 함수 이용 -> 이 문자열이 의미하는 list [dict1, dict2] 객체로 만들 수 있다. Series 객체의 `apply()`에 `literal_eval` 함수를 적용해 문자열을 객체로 변환한다."
      ],
      "metadata": {
        "id": "HZyl-eWieJ1H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval\n",
        "\n",
        "movies_df['genres']=movies_df['genres'].apply(literal_eval)\n",
        "movies_df['keywords']=movies_df['keywords'].apply(literal_eval)"
      ],
      "metadata": {
        "id": "14BLrLo_eqSZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "이제 genres 칼럼은 문자열이 아니라 실제 리스트 내부에 여러 장르 딕셔너리로 구성된 객체를 가진다. [{'id' : 28, 'name' : 'Action'}, {'id' : 12, 'name' : 'Adventure'}]와 같은 genres 칼럼에서 ['Action', 'Adventure']와 같은 장르명만 리스트 객체로 추출한다. genres 칼럼에서 'name' 키에 해당하는 값을 추출하기 위해 apply lambda 식을 이용한다. apply(lambda x : [y['name'] for y in x])와 같이 변환하면 리스트 내 여러 개의 딕셔너리의 'name' 키에 해당하는 값을 찾아 이를 리스트 객체로 변환한다."
      ],
      "metadata": {
        "id": "7pmZxKi-e2Bt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df['genres']=movies_df['genres'].apply(lambda x : [y['name'] for y in x])\n",
        "movies_df['keywords']=movies_df['keywords'].apply(lambda x : [y['name'] for y in x])\n",
        "movies_df[['genres', 'keywords']][:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "MR4cOtUCfVv9",
        "outputId": "3eb5b8c4-6d99-4200-e5af-f6334a57ec0d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          genres  \\\n",
              "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
              "\n",
              "                                                                                              keywords  \n",
              "0  [culture clash, future, space war, space colony, society, space travel, futuristic, romance, spa...  "
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              "\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-2acc17a6-6199-4146-ae2e-402f8470bb27 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-2acc17a6-6199-4146-ae2e-402f8470bb27');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"movies_df[['genres', 'keywords']][:1]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **장르 콘텐츠 유사도 측정**\n",
        "genres 칼럼은 여러 개의 개별 장르가 리스트로 구성돼 있다. 만약 영화 A의 genres가 [Action, Adventure, Fantasy, Science Fiction]으로 돼 있고, 영화 B의 genres가 [Adventure, Fantasy, Action]으로 돼 있다면 어떻게 장르별 유사도를 측정할까? 가장 간단한 방법은 genres를 문자열로 변경한 뒤 이를 CountVectorizer로 피처 벡터화한 행렬 데이터 값을 코사인 유사도로 비교하는 것이다. genres 칼럼을 기반으로 하는 콘텐츠 기반 필터링은 다음과 같은 단계로 구현한다.\n",
        "* 문자열로 변환된 genres 칼럼을 Count 기반으로 피처 벡터화 변환한다.\n",
        "* genres 문자열을 피처 벡터화 행렬로 변환한 데이터 세트를 코사인 유사도를 통해 비교한다. 이를 위해 데이터 세트의 레코드별로 타 레코드와 장르에서 코사인 유사도 값을 가지는 객체를 생성한다.\n",
        "* 장르 유사도가 높은 영화 중 평점이 높은 순으로 영화를 추천한다.\n",
        "\n",
        "먼저 genres 칼럼을 문자열로 변환한 뒤 사이킷런의 CountVectorizer를 이용해 피처 벡터 행렬로 만든다. 리스트 객체 값으로 구성된 genres 칼럼을 apply(lambda x : (' ').join(x))를 적용해 개별 요소를 공백 문자로 구분하는 문자열로 변환해 별도의 칼럼인 genres_listeral 칼럼으로 저장한다. 리스트 객체 내의 개별 값을 연속된 문자열로 변환하려면 일반적으로 ('구분문자').join(리스트 객체)를 사용하면 된다."
      ],
      "metadata": {
        "id": "iaa5VOOpftl7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Countvectorizer를 적용하기 위해 공백문자로 word 단위가 구분되는 문자열로 변환.\n",
        "movies_df['genres_literal']=movies_df['genres'].apply(lambda x : (' ').join(x))\n",
        "count_vect=CountVectorizer(min_df=1, ngram_range=(1, 2))\n",
        "genre_mat=count_vect.fit_transform(movies_df['genres_literal'])\n",
        "print(genre_mat.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHK4bdWqfsuG",
        "outputId": "bf35acda-f1b1-42cb-b2b6-e229c5b70df1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4803, 276)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "피처 벡터 행렬이 만들어졌다. 이렇게 생성된 피처 벡터 행렬에 사이킷런의 `cosine_similarity()`를 이용해 코사인 유사도를 계산한다. 사이킷런의 `cosine_similarity()` 함수는 기준 행과 비교 행의 코사인 유사도를 행렬 형태로 반환하는 함수이다."
      ],
      "metadata": {
        "id": "NxQ4qriGL3ts"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "iGRPbywKVLEB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "genre_sim=cosine_similarity(genre_mat, genre_mat)\n",
        "print(genre_sim.shape)\n",
        "print(genre_sim[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPm822XnVM6l",
        "outputId": "57fc7b25-b708-4a5b-cab1-cbfa55fabd5e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4803, 4803)\n",
            "[[1.         0.59628479 0.4472136  ... 0.         0.         0.        ]\n",
            " [0.59628479 1.         0.4        ... 0.         0.         0.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`cosine_similarity()` 호출로 생성된 genre_sim 객체는 movies_df의 genre_literal 칼럼을 피처 벡터화한 행렬(genre_mat) 데이터의 행(레코드)별 유사도 정보를 가지고 있으며, 결국은 movies_df DataFrame의 행렬 장르 유사도 값을 가지고 있는 것이다.\n",
        "\n",
        "movies_df를 장르 기준으로 콘텐츠 기반 필터링을 수행하려면 movies_df의 개별 레코드에 대해서 가장 장르 유사도가 높은 순으로 다른 레코드를 추출해야 하는데, 이를 위해 앞에서 생성한 genre_sim 객체를 이용한다.\n",
        "\n",
        "genre_sim 객체의 기준 행별로 비교 대상이 되는 행의 유사도 값이 높은 순으로 정렬된 행렬의 위치 인덱스 값을 추출하면 된다. 값이 높은 순으로 정렬된 비교 대상 행의 유사도 값이 아니라 비교 대상 행의 위치 인덱스임에 주의한다. 넘파이의 `argsort()[:, ::-1]`을 이용하면 유사도가 높은 순으로 정리된 genre_sim 객체의 비교 행 위치 인덱스 값을 간편하게 얻을 수 있다. `genre_sim.argsort()[:, ::-1]`를 사용해 높은 순으로 정렬된 비교 행 위치 인덱스 값을 가져오고 그중에 0번 레코드의 비교 행 위치 인덱스 값만 샘플로 추출해 보자.\n"
      ],
      "metadata": {
        "id": "F1jidf18VgMy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "genre_sim_sorted_ind=genre_sim.argsort()[:, ::-1]\n",
        "print(genre_sim_sorted_ind[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHvufNb-WuBJ",
        "outputId": "87336954-76c1-4451-b968-71cc6bee9099"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   0 3494  813 ... 3038 3037 2401]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "반환된 값이 의미하는 것은 0번 레코드의 경우 자신인 0번 레코드를 제외하면 3494번 레코드가 가장 유사도가 높고, 그다음이 813번, 가장 유사도가 낮은 레코드는 2401번 레코드라는 뜻이다.\n",
        "\n",
        "genre_sim_sorted_ind 객체는 각 레코드의 장르 코사인 유사도가 가장 높은 순으로 정렬된 타 레코드의 위치 인덱스 값을 가지고 있다. 이 위치 인덱스를 이용해 언제든지 특정 레코드와 코사인 유사도가 높은 다른 레코드를 추출할 수 있다."
      ],
      "metadata": {
        "id": "z_K8C_7iWz39"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **장르 콘텐츠 필터링을 이용한 영화 추천**\n",
        "장르 유사도에 따라 영화를 추천하는 함수를 생성한다. 함수명은 `find_sim_movie()`이며, 인자로 기반 데이터인 movies_df DataFrame, 레코드별 장르 코사인 유사도 인덱스를 가지고 있는 genre_sim_sorted_ind, 고객이 선정한 추천 기준이 되는 영화 제목, 추천할 영화 건수를 입력하면 추천 영화 정보를 가지는 DataFrame을 반환한다."
      ],
      "metadata": {
        "id": "EL7-g_wLXDyz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n",
        "  # 인자로 입력된 movies_df DataFrame에서 'title' 칼럼이 입력된 title_name 값인 DataFrame 추출\n",
        "  title_movie=df[df['title']==title_name]\n",
        "\n",
        "  # title_name을 가진 DataFrame의 index 객체를 ndarray로 반환하고\n",
        "  # sorted_ind 인자로 입력된 genre_sim_sorted_ind 객체에서 유사도 순으로 top_n개의 index 추출\n",
        "  title_index=title_movie.index.values\n",
        "  similar_indexes=sorted_ind[title_index, :(top_n)]\n",
        "\n",
        "  # 추출된 top_n index 출력. top_n index는 2차원 데이터임.\n",
        "  # dataframe에서 index로 사용하기 위해서 1차원 array로 변경\n",
        "  print(similar_indexes)\n",
        "  similar_indexes=similar_indexes.reshape(-1)\n",
        "\n",
        "  return df.iloc[similar_indexes]"
      ],
      "metadata": {
        "id": "4RhKF4qSXWQQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "`find_sim_movie()` 함수를 이용해 영화 '대부'와 장르별로 유사한 영화 10개를 추천해 보겠다. `find_sim_movie(dataframe=movies, sorted_ind=genre_sim_sorted_ind, title='The Godfather', top_n=10)`을 호출한다."
      ],
      "metadata": {
        "id": "7rTasBlAX_2L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "similar_movies=find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
        "similar_movies[['title', 'vote_average']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "az1H05ZcYKXb",
        "outputId": "5722f76b-dc9b-4e8e-955d-2cf31eafce50"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2731 1243 3636 1946 2640 4065 1847 4217  883 3866]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  vote_average\n",
              "2731                          The Godfather: Part II           8.3\n",
              "1243                                    Mean Streets           7.2\n",
              "3636                                   Light Sleeper           5.7\n",
              "1946  The Bad Lieutenant: Port of Call - New Orleans           6.0\n",
              "2640         Things to Do in Denver When You're Dead           6.7\n",
              "4065                                      Mi America           0.0\n",
              "1847                                      GoodFellas           8.2\n",
              "4217                                            Kids           6.8\n",
              "883                              Catch Me If You Can           7.7\n",
              "3866                                     City of God           8.1"
            ],
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              "\n",
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              "      <th>1946</th>\n",
              "      <td>The Bad Lieutenant: Port of Call - New Orleans</td>\n",
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              "      <td>7.7</td>\n",
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              "    <tr>\n",
              "      <th>3866</th>\n",
              "      <td>City of God</td>\n",
              "      <td>8.1</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb08f54e-2825-43bc-8414-60e656ff7157');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b400b3d3-34ea-41a0-b6f5-c2296263bc7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Catch Me If You Can\",\n          \"Mean Streets\",\n          \"Mi America\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4467892793981623,\n        \"min\": 0.0,\n        \"max\": 8.3,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7.7,\n          7.2,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "'대부 2편'이 가장 먼저 추천됐다. 그 외에 1847번 인덱스의 '좋은 친구들'도 대부와 비슷한 유형으로, 대부를 재미있게 봤다면 이 두 가지 모두 추천해야 할 영화일 것이다. 하지만 낯선 영화도 많다. '라이트 슬리퍼', 'Mi America', 'Kids' 등 대부를 좋아하는 고객에게 섣불리 추천하기에는 이해하기 어려운 영화도 있다. '라이트 슬리퍼'의 경우 평점이 낮은 편이고, 게다가 'Mi America'의 경우에는 평점이 0이다. 좀 더 개선이 필요하다.\n",
        "\n",
        "이번에는 좀 더 많은 후보군을 선정한 뒤에 영화의 평점에 따라 필터링해서 최종 추천하는 방식으로 변경한다. 영화의 평점 정보인 'vote_average' 값을 이용한다. vote_average는 0부터 10점 만점까지의 점수로 되어 있는데, 여러 관객이 평가한 평점을 평균한 것이다. 그런데 1명, 2명의 소수의 관객이 특정 영화에 만점이나 매우 높은 평점을 부여해 왜곡된 데이터를 가지고 있다. 이를 확인하기 위해 `sort_values()`를 이용해 평점('vote_average') 오름차순으로 movies_df를 정렬해서 10개만 출력해 보자."
      ],
      "metadata": {
        "id": "bi07YR-iYTs9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df[['title', 'vote_average', 'vote_count']].sort_values('vote_average',\n",
        "                                                              ascending=False)[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "rDXJdssgZEiU",
        "outputId": "e1211599-de85-4ad2-c2c9-c69ee327256a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         title  vote_average  vote_count\n",
              "3519          Stiff Upper Lips          10.0           1\n",
              "4247     Me You and Five Bucks          10.0           2\n",
              "4045     Dancer, Texas Pop. 81          10.0           1\n",
              "4662            Little Big Top          10.0           1\n",
              "3992                 Sardaarji           9.5           2\n",
              "2386            One Man's Hero           9.3           2\n",
              "2970        There Goes My Baby           8.5           2\n",
              "1881  The Shawshank Redemption           8.5        8205\n",
              "2796     The Prisoner of Zenda           8.4          11\n",
              "3337             The Godfather           8.4        5893"
            ],
            "text/html": [
              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3519</th>\n",
              "      <td>Stiff Upper Lips</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4247</th>\n",
              "      <td>Me You and Five Bucks</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4045</th>\n",
              "      <td>Dancer, Texas Pop. 81</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4662</th>\n",
              "      <td>Little Big Top</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3992</th>\n",
              "      <td>Sardaarji</td>\n",
              "      <td>9.5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2386</th>\n",
              "      <td>One Man's Hero</td>\n",
              "      <td>9.3</td>\n",
              "      <td>2</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>1881</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8205</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",
              "    </tr>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"                                                              ascending=False)[:10]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"The Prisoner of Zenda\",\n          \"Me You and Five Bucks\",\n          \"One Man's Hero\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7366591251499343,\n        \"min\": 8.4,\n        \"max\": 10.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          9.5,\n          8.4,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3020,\n        \"min\": 1,\n        \"max\": 8205,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5893,\n          8205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "'쇼생크 탈출'이나 '대부' 같은 명작보다 높은 순위에 'Stiff Upper Lips', 'Me You and Five Bucks'와 같이 이름도 들어본 적 없는 영화가 더 높은 평점으로 있다. 이들은 모두 평가 횟수가 매우 작다. 이와 같은 왜곡된 평점 데이터를 회피할 수 있도록 평점에 평가 횟수를 반영할 수 있는 새로운 평가 방식이 필요하다.\n",
        "\n",
        "유명한 영화 평점 사이트인 IMDB에서는 평가 횟수에 대한 가중치가 부여된 평점 방식을 이용한다. 이 방식을 이용해 새롭게 평점을 부여한다. 가중 평점의 공식은 다음과 같다.\n",
        "\n",
        "$$가중 평점=(v/(v+m))*R+(m/(v+m))*C$$\n",
        "\n",
        "* v : 개별 영화에 평점을 투표한 횟수\n",
        "* m : 평점을 부여하기 위한 최소 투표 횟수\n",
        "* R : 개별 영화에 대한 평균 평점\n",
        "* C : 전체 영화에 대한 평균 평점\n",
        "\n",
        "V는 movies_df의 'vote_count' 값이며, R 값은 'vote_average' 값에 해당한다. C의 경우 전체 영화의 평균 평점이므로 movie_df['vote_average'].mean()으로 구할 수 있다. m의 경우는 투표 횟수에 따른 가중치를 직접 조절하는 역할을 하는데, m 값을 높이면 평점 투표 횟수가 많은 영화에 더 많은 가중 평점을 부여한다. m 값은 전체 투표 횟수에서 상위 60%에 해당하는 횟수를 기준으로 정한다. 상위 60% 값은 Series 객체의 `quantile()`을 이용해 추출한다."
      ],
      "metadata": {
        "id": "7Kfnp2grZP8F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "C=movies_df['vote_average'].mean()\n",
        "m=movies_df['vote_count'].quantile(0.6)\n",
        "print('C:', round(C, 3), 'm:', round(m, 3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Aa6xzFAaHMJ",
        "outputId": "9fb46e95-e668-4b79-806a-d7c375303634"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C: 6.092 m: 370.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "기존 평점을 새로운 가중 평점으로 변경하는 함수를 생성하고 이를 이용해 새로운 평점 정보인 'vote_weighted' 값을 만든다. 함수명은 `weighted_vote_average()`이다. 이 함수는 DataFrame의 레코드를 인자로 받아 이 레코드의 vote_count와 vote_averate 칼럼, 그리고 미리 추출된 m과 C 값을 적용해 레코드별 가중 평점을 반환한다. 해당 함수를 movies_df의 `apply()` 함수의 인자로 입력해 가중 평점을 계산한다."
      ],
      "metadata": {
        "id": "88pKZuRMaXoI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentile=0.6\n",
        "m=movies_df['vote_count'].quantile(percentile)\n",
        "C=movies_df['vote_average'].mean()\n",
        "\n",
        "def weighted_vote_average(record):\n",
        "  v=record['vote_count']\n",
        "  R=record['vote_average']\n",
        "\n",
        "  return ((v/(v+m))*R)+((m/(m+v))*C)\n",
        "\n",
        "movies_df['weighted_vote']=movies.apply(weighted_vote_average, axis=1)"
      ],
      "metadata": {
        "id": "i4tHfh1uarZ2"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "새롭게 부여된 weighted_vote 평점이 높은 순으로 상위 10개의 영화를 추출한다."
      ],
      "metadata": {
        "id": "wXjftLJqbHQ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df[['title', 'vote_average', 'weighted_vote', 'vote_count']].sort_values(\n",
        "    'weighted_vote', ascending=False)[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "H88sg4IbbW3q",
        "outputId": "15e4a60c-6e4f-4560-a71c-14f6ad31fb3c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         title  vote_average  weighted_vote  vote_count\n",
              "1881  The Shawshank Redemption           8.5       8.396052        8205\n",
              "3337             The Godfather           8.4       8.263591        5893\n",
              "662                 Fight Club           8.3       8.216455        9413\n",
              "3232              Pulp Fiction           8.3       8.207102        8428\n",
              "65             The Dark Knight           8.2       8.136930       12002\n",
              "1818          Schindler's List           8.3       8.126069        4329\n",
              "3865                  Whiplash           8.3       8.123248        4254\n",
              "809               Forrest Gump           8.2       8.105954        7927\n",
              "2294             Spirited Away           8.3       8.105867        3840\n",
              "2731    The Godfather: Part II           8.3       8.079586        3338"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>662</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3232</th>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.207102</td>\n",
              "      <td>8428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.136930</td>\n",
              "      <td>12002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1818</th>\n",
              "      <td>Schindler's List</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.126069</td>\n",
              "      <td>4329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3865</th>\n",
              "      <td>Whiplash</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.123248</td>\n",
              "      <td>4254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>809</th>\n",
              "      <td>Forrest Gump</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.105954</td>\n",
              "      <td>7927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2294</th>\n",
              "      <td>Spirited Away</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.105867</td>\n",
              "      <td>3840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2731</th>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.079586</td>\n",
              "      <td>3338</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    'weighted_vote', ascending=False)[:10]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Spirited Away\",\n          \"The Godfather\",\n          \"Schindler's List\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08755950357709151,\n        \"min\": 8.2,\n        \"max\": 8.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          8.4,\n          8.2,\n          8.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09696608479450805,\n        \"min\": 8.07958629828635,\n        \"max\": 8.39605162693645,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          8.105867158639835,\n          8.263590802034972,\n          8.126068673669016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2866,\n        \"min\": 3338,\n        \"max\": 12002,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3840,\n          5893,\n          4329\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TOP 10에 대한 개인별 성향이 조금씩 달라서 위 결과에 이의가 있을지는 몰라도 위 영화 모두 매우 뛰어난 영화라는 점에는 이견이 없을 것이다. 이제 새롭게 정의된 평점 기준에 따라 영화를 추천해 보겠다. 장르 유사성이 높은 영화를 top_n의 2배수만큼 후보군으로 선정한 뒤에 weighted_vote 칼럼 값이 높은 순으로 top_n만큼 추출하는 방식으로 `find_sim_movie()` 함수를 변경한다. 변경된 `find_sim_movie()`를 이용해 다시 한 번 '대부'와 유사한 영화를 콘텐츠 기반 필터링 방식으로 추천해 보겠다."
      ],
      "metadata": {
        "id": "jY3uDVLbbhYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def find_sim_movie(df, sorted_ind, title_name, top_n=10):\n",
        "  title_movie=df[df['title']==title_name]\n",
        "  title_index=title_movie.index.values\n",
        "\n",
        "  # top_n의 2배에 해당하는 장르 유사성이 높은 인덱스 추출\n",
        "  similar_indexes=sorted_ind[title_index, :(top_n*2)]\n",
        "  similar_indexes=similar_indexes.reshape(-1)\n",
        "  # 기준 영화 인덱스는 제외\n",
        "  similar_indexes=similar_indexes[similar_indexes != title_index]\n",
        "\n",
        "  # top_n의 2배에 해당하는 후보군에서 weighted_vote가 높은 순으로 top_n만큼 추출\n",
        "  return df.iloc[similar_indexes].sort_values('weighted_vote', ascending=False)[:top_n]\n",
        "\n",
        "similar_movies=find_sim_movie(movies_df, genre_sim_sorted_ind, 'The Godfather', 10)\n",
        "similar_movies[['title', 'vote_average', 'weighted_vote']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "u53LRtJqb4HB",
        "outputId": "001de9be-6ad7-483a-d01a-cee4c5566302"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            title  vote_average  weighted_vote\n",
              "2731       The Godfather: Part II           8.3       8.079586\n",
              "1847                   GoodFellas           8.2       7.976937\n",
              "3866                  City of God           8.1       7.759693\n",
              "1663  Once Upon a Time in America           8.2       7.657811\n",
              "883           Catch Me If You Can           7.7       7.557097\n",
              "281             American Gangster           7.4       7.141396\n",
              "4041              This Is England           7.4       6.739664\n",
              "1149              American Hustle           6.8       6.717525\n",
              "1243                 Mean Streets           7.2       6.626569\n",
              "2839                     Rounders           6.9       6.530427"
            ],
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a8681c1f-d70c-44df-9748-4d3e4326495e 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-a8681c1f-d70c-44df-9748-4d3e4326495e');\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",
              "\n",
              "\n",
              "<div id=\"df-40f67d48-7b1f-439e-9de4-8411901281b8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-40f67d48-7b1f-439e-9de4-8411901281b8')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-40f67d48-7b1f-439e-9de4-8411901281b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"similar_movies[['title', 'vote_average', 'weighted_vote']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Mean Streets\",\n          \"GoodFellas\",\n          \"American Gangster\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5613475849338901,\n        \"min\": 6.8,\n        \"max\": 8.3,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.2,\n          6.8,\n          8.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weighted_vote\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5954507780784589,\n        \"min\": 6.530427473190107,\n        \"max\": 8.07958629828635,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.626568667932654,\n          7.976937256676415,\n          7.1413961709782265\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "이전에 추천된 영화보다 훨씬 나은 영화가 추천됐다. 특히 '원스 어폰 어 타임 인 아메리카'가 추천됐는데, 대부를 좋아하는 사람이라면 공감할 만한 추천 영화이다. 하지만 장르만으로 영화가 전달하는 많은 요소와 분위기, 그리고 개인이 좋아하는 성향을 반영하기에는 부족할 수 있다. 아마 좋아하는 영화배우나 감독을 보고 영화를 선택하는 경우가 더 많을 것이다. 앞의 장르를 기반으로 한 콘텐츠 필터링 예제를 좀 더 다양한 콘텐츠 기반으로 확장할 수 있다."
      ],
      "metadata": {
        "id": "gY_JN48qcof-"
      }
    }
  ]
}