Word2vec을_이용한_추천시스템.ipynb

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      "name": "Word2vec을 이용한 추천시스템.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/euna-jeong20/Intro_Recommender_System/blob/main/Word2vec%EC%9D%84_%EC%9D%B4%EC%9A%A9%ED%95%9C_%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYUkt0PXwO30"
      },
      "source": [
        "* 컨텐츠 기반 모델"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjMq0_RJgFHq"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import gensim   #WORD2VEC의 알고리즘을 호출해주는 패키지"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMajoMifibKs"
      },
      "source": [
        "path = '/content/drive/MyDrive/recommender_system/movies/'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "height": 112
        },
        "id": "r2hyD0fKjGtx",
        "outputId": "11719a89-2a7d-41aa-f44d-499ba148ac91"
      },
      "source": [
        "movie = pd.read_csv(path + 'ratings.csv', low_memory=False)\n",
        "movie.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
              "1       1     1029     3.0  1260759179"
            ]
          },
          "metadata": {},
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        "id": "Z321SAIzjOhC",
        "outputId": "95bf7447-b2f0-4ec3-d025-42a6f0201068"
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      "source": [
        "#시간순으로 정렬\n",
        "movie = movie.sort_values(by='timestamp', ascending=True).reset_index(drop=True)\n",
        "movie.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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            "text/plain": [
              "   userId  movieId  rating  timestamp\n",
              "0     383       21     3.0  789652009\n",
              "1     383       47     5.0  789652009\n",
              "2     383     1079     3.0  789652009\n",
              "3     409       85     5.0  828212412\n",
              "4     409       35     4.0  828212412"
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        "id": "dXnHo4M0jfEb",
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      },
      "source": [
        "#영화의 metadata를 불러와서 movieID에 맞는 TITLE을 구한다.\n",
        "meta = pd.read_csv(path + 'movies_metadata.csv', low_memory=False)\n",
        "meta.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>adult</th>\n",
              "      <th>belongs_to_collection</th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>imdb_id</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>poster_path</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>production_countries</th>\n",
              "      <th>release_date</th>\n",
              "      <th>revenue</th>\n",
              "      <th>runtime</th>\n",
              "      <th>spoken_languages</th>\n",
              "      <th>status</th>\n",
              "      <th>tagline</th>\n",
              "      <th>title</th>\n",
              "      <th>video</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
              "      <td>30000000</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "      <td>http:https://toystory.disney.com/toy-story</td>\n",
              "      <td>862</td>\n",
              "      <td>tt0114709</td>\n",
              "      <td>en</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
              "      <td>21.946943</td>\n",
              "      <td>/rhIRbceoE9lR4veEXuwCC2wARtG.jpg</td>\n",
              "      <td>[{'name': 'Pixar Animation Studios', 'id': 3}]</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-10-30</td>\n",
              "      <td>373554033.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Toy Story</td>\n",
              "      <td>False</td>\n",
              "      <td>7.7</td>\n",
              "      <td>5415.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65000000</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8844</td>\n",
              "      <td>tt0113497</td>\n",
              "      <td>en</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "      <td>17.015539</td>\n",
              "      <td>/vzmL6fP7aPKNKPRTFnZmiUfciyV.jpg</td>\n",
              "      <td>[{'name': 'TriStar Pictures', 'id': 559}, {'na...</td>\n",
              "      <td>[{'iso_3166_1': 'US', 'name': 'United States o...</td>\n",
              "      <td>1995-12-15</td>\n",
              "      <td>262797249.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
              "      <td>Released</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>False</td>\n",
              "      <td>6.9</td>\n",
              "      <td>2413.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   adult  ... vote_count\n",
              "0  False  ...     5415.0\n",
              "1  False  ...     2413.0\n",
              "\n",
              "[2 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lltNKWtDkakD",
        "outputId": "774775b4-16e0-4773-e901-d60972bea245"
      },
      "source": [
        "meta.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['adult', 'belongs_to_collection', 'budget', 'genres', 'homepage', 'id',\n",
              "       'imdb_id', 'original_language', 'original_title', 'overview',\n",
              "       'popularity', 'poster_path', 'production_companies',\n",
              "       'production_countries', 'release_date', 'revenue', 'runtime',\n",
              "       'spoken_languages', 'status', 'tagline', 'title', 'video',\n",
              "       'vote_average', 'vote_count'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CyfDSUZOlITL"
      },
      "source": [
        "meta = meta.rename(columns={'id' : 'movieId'})      # 둘의 col명을 맞춰주기위해\n",
        "movie['movieId'] = movie['movieId'].astype(str)     # str형태로 바꿔준다\n",
        "meta['movieId'] = meta['movieId'].astype(str)       # str형태로 바꿔준다\n",
        "\n",
        "movie = pd.merge(movie, meta[['movieId', 'original_title']], how='left', on='movieId')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 206
        },
        "id": "kLJWRhvKm38Y",
        "outputId": "66fc20eb-62f7-44b0-b326-a99f004cc781"
      },
      "source": [
        "movie.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
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              "      <td>383</td>\n",
              "      <td>21</td>\n",
              "      <td>3.0</td>\n",
              "      <td>789652009</td>\n",
              "      <td>The Endless Summer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>383</td>\n",
              "      <td>47</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>409</td>\n",
              "      <td>85</td>\n",
              "      <td>5.0</td>\n",
              "      <td>828212412</td>\n",
              "      <td>Raiders of the Lost Ark</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>409</td>\n",
              "      <td>35</td>\n",
              "      <td>4.0</td>\n",
              "      <td>828212412</td>\n",
              "      <td>The Simpsons Movie</td>\n",
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            ],
            "text/plain": [
              "   userId movieId  rating  timestamp           original_title\n",
              "0     383      21     3.0  789652009       The Endless Summer\n",
              "1     383      47     5.0  789652009                      NaN\n",
              "2     383    1079     3.0  789652009                      NaN\n",
              "3     409      85     5.0  828212412  Raiders of the Lost Ark\n",
              "4     409      35     4.0  828212412       The Simpsons Movie"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EClb0klsnLPm"
      },
      "source": [
        "movie = movie[movie['original_title'].notnull()].reset_index(drop=True)     #'original_title'의 결측치 제거"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "FDwLmrJMnWHN",
        "outputId": "1295768a-2ae2-4669-c33f-bc39e8535384"
      },
      "source": [
        "agg = movie.groupby(['userId'])['original_title'].agg({'unique'})\n",
        "agg.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
              "      <td>[Jay and Silent Bob Strike Back, Vivement dima...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[48 Hrs., The Hours, Terminator 3: Rise of the...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[300, The Killing, Shortbus, Finding Neverland...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[David, The Wedding Planner, Casablanca, Sleep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[Gleaming the Cube, Cool Hand Luke, Hidalgo, U...</td>\n",
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            "text/plain": [
              "                                                   unique\n",
              "userId                                                   \n",
              "1       [Jay and Silent Bob Strike Back, Vivement dima...\n",
              "2       [48 Hrs., The Hours, Terminator 3: Rise of the...\n",
              "3       [300, The Killing, Shortbus, Finding Neverland...\n",
              "4       [David, The Wedding Planner, Casablanca, Sleep...\n",
              "5       [Gleaming the Cube, Cool Hand Luke, Hidalgo, U..."
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fccywziLoMSS"
      },
      "source": [
        "* 한 사람마다 영화를 뭘 봤고 무슨 순서로 봤는지 알 수 있다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "rqY2ySo1nhk4",
        "outputId": "e2ca20d7-8a58-414b-bfb9-a8490585a7e0"
      },
      "source": [
        "movie['original_title'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['The Endless Summer', 'Raiders of the Lost Ark',\n",
              "       'The Simpsons Movie', ..., 'The Lonedale Operator',\n",
              "       'Violeta se fue a los cielos', 'To Kill a Priest'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkdLKSi6oYKG"
      },
      "source": [
        "# str로 변경\n",
        "sentence = []\n",
        "\n",
        "for user_sentence in agg['unique'].values:\n",
        "    sentence.append(list(map(str, user_sentence)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NxIqmRKhpIzr",
        "outputId": "1990ae4a-5c88-4afd-e4a1-bc441669ea7f"
      },
      "source": [
        "sentence[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Jay and Silent Bob Strike Back',\n",
              " 'Vivement dimanche!',\n",
              " 'Rocky III',\n",
              " 'American Pie',\n",
              " 'My Tutor',\n",
              " 'Greed']"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ii-idt2Boluf"
      },
      "source": [
        "from gensim.models import Word2Vec\n",
        "\n",
        "embedding_model = Word2Vec(sentence, size=20, window=5,\n",
        "                           min_count=1, workers=4, iter=200, sg=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sK8_KEi1r4f2"
      },
      "source": [
        "* size = 워드 벡터의 특징 값. 즉, 임베딩 된 벡터의 차원.\n",
        "* window = 양 옆 주변단어들을 몇개 볼지\n",
        "* min_count = 단어 최소 빈도 수 제한 (빈도가 적은 단어들은 학습하지 않는다.)\n",
        "* workers = 학습을 위한 프로세스 수\n",
        "* sg = 0은 CBOW, 1은 Skip-gram."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orlHNYbjpAyO",
        "outputId": "ef3cc7d1-c098-45d6-dc9f-e667d8896ee6"
      },
      "source": [
        "embedding_model.wv.most_similar(positive=['Spider-Man 2'], topn=10)     #wv는 가중치 벡터"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Snow Cake', 0.8830479383468628),\n",
              " ('Rumor Has It...', 0.8176966905593872),\n",
              " ('Conquest of the Planet of the Apes', 0.7685685157775879),\n",
              " ('Licence to Kill', 0.7657096982002258),\n",
              " ('To Die For', 0.7570158839225769),\n",
              " ('National Treasure', 0.7389312982559204),\n",
              " ('Dark Star', 0.7324922680854797),\n",
              " ('The Godfather', 0.7314088344573975),\n",
              " ('Heavenly Creatures', 0.7295575141906738),\n",
              " ('Harry Potter and the Prisoner of Azkaban', 0.7289674878120422)]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}