클로즈업_추천시스템.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cyp4y7fIGcXe",
        "outputId": "1518b34c-95be-48d2-8a5f-72526bef6735"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/Commit_Test_Folder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nK75qTr0GXGa",
        "outputId": "85b0baad-eafc-4923-e4d8-8b815cc5718d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Commit_Test_Folder\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://closeup-raffle: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@github.com/closeup-raffle/AI.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLsKmb64JD3v",
        "outputId": "cf20b1f3-633d-4d23-9c36-8f9689906656"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'AI'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (3/3), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://(closeup-raffle):(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)@github.com/(closeup-raffle)/(AI).git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-kjgKNdHCbQ",
        "outputId": "da5a3de9-c627-4d9b-ae9a-8396da5dac34"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 1: syntax error near unexpected token `('\n",
            "/bin/bash: -c: line 1: `git clone https://(closeup-raffle):(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)@github.com/(closeup-raffle)/(AI).git'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd AI"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljEb_HTRJp5M",
        "outputId": "79416c11-d306-4976-ba89-3549a1501deb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Commit_Test_Folder/AI\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.email '2874024@naver.com'\n",
        "!git config --global user.name 'khwee2000'"
      ],
      "metadata": {
        "id": "8ttGufh8J1jU"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git add Test_code.ipynb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7PU84JlKCpF",
        "outputId": "31438b79-cfea-4298-a30b-91e625924cad"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: pathspec 'Test_code.ipynb' did not match any files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git commit -m '원하는 아무 메시지'\n",
        "!git push"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74PuCQKnKJfa",
        "outputId": "accc0d3b-664a-4d5a-8b48-84bd0ac8f355"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On branch main\n",
            "Your branch is up to date with 'origin/main'.\n",
            "\n",
            "nothing to commit, working tree clean\n",
            "Everything up-to-date\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 유저 임시 데이터 생성\n",
        "- columns (ID/ User_Id, User_Age, User_SNS_Genre, User_content_Genre, User_Gender)\n"
      ],
      "metadata": {
        "id": "nIyQz7ICKmvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "# 데이터 프레임 생성\n",
        "data = {\n",
        "    'Id': ['train_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'User_Id': ['User_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'User_Age': [random.choice([random.randint(10, 19), random.randint(20, 29)]) for _ in range(10000)],\n",
        "    'User_Sns_Genre': [random.sample(['insta', 'facebook', 'youtube', 'tiktok', 'twitch'], random.randint(1, 5)) for _ in range(10000)],\n",
        "    'User_Content_Genre': [random.sample(['food', 'travel', 'fashion', 'vlog', 'game'], random.randint(1, 5)) for _ in range(10000)],\n",
        "    'User_Gender': [random.choice(['Male', 'Female']) for _ in range(10000)]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# 데이터프레임을 CSV 파일로 저장\n",
        "df.to_csv('user_data.csv', index=False)\n"
      ],
      "metadata": {
        "id": "1JcxK9lxHWgb"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 콘텐츠 임시 데이터 생성\n",
        "- columns(Id, User_Id, Raffle_Id, Raffle_Genre, Content_Genre, Creater_Age, Creat_Id, Click_Date)"
      ],
      "metadata": {
        "id": "cVt06Qx5LIkR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "# 데이터 프레임 생성\n",
        "data = {\n",
        "    'Id': ['train_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'User_Id': ['User_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'Raffle_Id': ['raffle_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'Raffle_Genre': [random.choice(['genre1', 'genre2', 'genre3', 'genre4', 'genre5']) for _ in range(10000)],\n",
        "    'Content_Genre': [random.choice(['food', 'travel', 'fashion', 'vlog', 'game']) for _ in range(10000)],\n",
        "    'Creater_Age': [random.choice([random.randint(10, 19), random.randint(20, 29)]) for _ in range(10000)],\n",
        "    'Creater_Id': ['Creater_' + str(i).zfill(6) for i in range(1, 10001)],\n",
        "    'Click_Date': [(datetime(2023, 9, 1) + timedelta(days=random.randint(1, 30))).strftime('%Y-%m-%d') for _ in range(10000)],\n",
        "    'Raffle_Done': [random.choice(['o', 'x']) for _ in range(10000)],\n",
        "    'Cost_Point': [random.randint(100, 5000) for _ in range(10000)]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# 데이터프레임을 CSV 파일로 저장\n",
        "df.to_csv('raffle_data.csv', index=False)"
      ],
      "metadata": {
        "id": "OiCrihDxIbM7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 협업 필터링"
      ],
      "metadata": {
        "id": "KV1mqVRUP82D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# 데이터 로드\n",
        "data = pd.read_csv('raffle_data.csv')\n",
        "\n",
        "# 사용자-항목 평점 행렬 생성\n",
        "user_item_matrix = pd.pivot_table(data, values='Cost_Point', index='User_Id', columns='Raffle_Id')"
      ],
      "metadata": {
        "id": "Gk7a5u9VKRAk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 유사도 행렬 계산\n",
        "user_similarity = cosine_similarity(user_item_matrix.fillna(0))\n",
        "\n",
        "# 숫자 인덱스를 사용자 ID로 매핑하는 딕셔너리 생성\n",
        "user_id_to_index = {user_id: i for i, user_id in enumerate(user_item_matrix.index)}\n",
        "index_to_user_id = {i: user_id for i, user_item_id in enumerate(user_item_matrix.index)}\n",
        "\n",
        "# 유사도를 기반으로 사용자에게 아직 평가하지 않은 항목 예측\n",
        "def get_user_recommendations(user_id, user_similarity, user_item_matrix, user_id_to_index, index_to_user_id):\n",
        "    user_index = user_id_to_index.get(user_id)\n",
        "    if user_index is None:\n",
        "        print(\"User ID not found.\")\n",
        "        return []\n",
        "\n",
        "    similar_users = user_similarity[user_index]\n",
        "    rated_items = user_item_matrix.loc[index_to_user_id[user_index]].notna()\n",
        "\n",
        "    # 사용자가 이미 평가한 항목 제외\n",
        "    similar_users = similar_users * (~rated_items)\n",
        "\n",
        "    # 유사한 사용자 중 예측 평점이 높은 항목 선택\n",
        "    recommended_items = user_item_matrix.columns[similar_users.argsort()[::-1]]\n",
        "\n",
        "    return recommended_items\n",
        "\n",
        "# 예시: User_000001 사용자에게 추천\n",
        "user_id = 'User_000001'\n",
        "recommended_items = get_user_recommendations(user_id, user_similarity, user_item_matrix, user_id_to_index, index_to_user_id)\n",
        "print(\"Recommended items for\", user_id, \":\", recommended_items)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKUS9Yf_Lgxi",
        "outputId": "b32297d7-2e7c-4039-b050-c7cc1dc4dc3e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recommended items for User_000001 : Index(['raffle_010000', 'raffle_003330', 'raffle_003337', 'raffle_003336',\n",
            "       'raffle_003335', 'raffle_003334', 'raffle_003333', 'raffle_003332',\n",
            "       'raffle_003331', 'raffle_003329',\n",
            "       ...\n",
            "       'raffle_006672', 'raffle_006671', 'raffle_006670', 'raffle_006669',\n",
            "       'raffle_006668', 'raffle_006667', 'raffle_006666', 'raffle_006665',\n",
            "       'raffle_006664', 'raffle_000001'],\n",
            "      dtype='object', name='Raffle_Id', length=10000)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 콘텐츠 추천시스템"
      ],
      "metadata": {
        "id": "eOiJcZ-IQANC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "# 데이터 로드\n",
        "data = pd.read_csv('raffle_data.csv')\n",
        "\n",
        "# TF-IDF 벡터화\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "tfidf_matrix = tfidf.fit_transform(data['Content_Genre'])\n",
        "\n",
        "# 항목 간 유사성 계산\n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
        "\n",
        "# 항목 및 사용자 ID 매핑 딕셔너리 생성\n",
        "item_id_to_index = {raffle_id: i for i, raffle_id in enumerate(data['Raffle_Id'])}\n",
        "index_to_item_id = {i: raffle_id for i, raffle_id in enumerate(data['Raffle_Id'])}\n",
        "\n",
        "# 컨텐츠 기반 필터링으로 아이템 추천\n",
        "def get_content_based_recommendations(item_id, cosine_sim, data, item_id_to_index, index_to_item_id):\n",
        "    item_index = item_id_to_index.get(item_id)\n",
        "    if item_index is None:\n",
        "        print(\"Item ID not found.\")\n",
        "        return []\n",
        "\n",
        "    # 선택한 항목과 다른 항목 간의 유사성 가져오기\n",
        "    sim_scores = list(enumerate(cosine_sim[item_index]))\n",
        "\n",
        "    # 유사성에 따라 항목 정렬\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # 가장 유사한 항목 선택 (자신 제외)\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # 선택된 항목의 인덱스를 실제 항목 ID로 변환\n",
        "    recommended_items = [index_to_item_id[i[0]] for i in sim_scores]\n",
        "\n",
        "    return recommended_items\n",
        "\n",
        "# 예시: raffle_000001 항목에 대한 컨텐츠 기반 추천\n",
        "item_id = 'raffle_000001'\n",
        "recommended_items = get_content_based_recommendations(item_id, cosine_sim, data, item_id_to_index, index_to_item_id)\n",
        "print(\"Content-based recommended items for\", item_id, \":\", recommended_items)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBLs4KuPL02C",
        "outputId": "7f2dff4a-a21b-48de-e236-e58291897d43"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Content-based recommended items for raffle_000001 : ['raffle_000007', 'raffle_000008', 'raffle_000015', 'raffle_000021', 'raffle_000026', 'raffle_000029', 'raffle_000031', 'raffle_000036', 'raffle_000039', 'raffle_000044']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install surprise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDLve6BiPFck",
        "outputId": "0adf9256-743b-4da4-ba3c-1965f843cded"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting surprise\n",
            "  Downloading surprise-0.1-py2.py3-none-any.whl (1.8 kB)\n",
            "Collecting scikit-surprise (from surprise)\n",
            "  Downloading scikit-surprise-1.1.3.tar.gz (771 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m772.0/772.0 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: joblib>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.3.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-surprise->surprise) (1.11.2)\n",
            "Building wheels for collected packages: scikit-surprise\n",
            "  Building wheel for scikit-surprise (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.3-cp310-cp310-linux_x86_64.whl size=3156233 sha256=afade97fc9091299a09da51ef1c46a34080b6ae2aff4958d851a8f2032a9f070\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/ca/a8/4e28def53797fdc4363ca4af740db15a9c2f1595ebc51fb445\n",
            "Successfully built scikit-surprise\n",
            "Installing collected packages: scikit-surprise, surprise\n",
            "Successfully installed scikit-surprise-1.1.3 surprise-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 하이브리드 추천시스템"
      ],
      "metadata": {
        "id": "PyCNKSm2QDF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from surprise import Reader, Dataset, SVD\n",
        "from surprise.model_selection import train_test_split\n",
        "\n",
        "# 데이터 로드\n",
        "data = pd.read_csv('raffle_data.csv')\n",
        "\n",
        "# 사용자-항목 평점 행렬 생성\n",
        "reader = Reader(rating_scale=(data['Cost_Point'].min(), data['Cost_Point'].max()))\n",
        "user_item_data = Dataset.load_from_df(data[['User_Id', 'Raffle_Id', 'Cost_Point']], reader)\n",
        "trainset, testset = train_test_split(user_item_data, test_size=0.2)\n",
        "\n",
        "# SVD 모델 학습\n",
        "model = SVD()\n",
        "model.fit(trainset)\n",
        "\n",
        "# 컨텐츠 기반 필터링\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "tfidf_matrix = tfidf.fit_transform(data['Content_Genre'])\n",
        "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
        "\n",
        "# 사용자에게 협업 필터링으로 추천 항목 생성\n",
        "def get_collaborative_filtering_recommendations(user_id, model, trainset, data, n=10):\n",
        "    testset = trainset.build_anti_testset()\n",
        "    testset = [elem for elem in testset if elem[0] == user_id]\n",
        "    predictions = model.test(testset)\n",
        "    predictions.sort(key=lambda x: x.est, reverse=True)\n",
        "    top_predictions = predictions[:n]\n",
        "    recommended_items = [pred.iid for pred in top_predictions]\n",
        "    return recommended_items\n",
        "\n",
        "# 사용자에게 컨텐츠 기반 필터링으로 추천 항목 생성\n",
        "def get_content_based_recommendations(item_id, cosine_sim, data, n=10):\n",
        "    item_index = data[data['Raffle_Id'] == item_id].index[0]\n",
        "    sim_scores = list(enumerate(cosine_sim[item_index]))\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    top_sim_scores = sim_scores[1:n+1]\n",
        "    recommended_items = [data.iloc[score[0]]['Raffle_Id'] for score in top_sim_scores]\n",
        "    return recommended_items\n",
        "\n",
        "# 하이브리드 추천 시스템\n",
        "def hybrid_recommendations(user_id, model, trainset, data, cosine_sim, n=10):\n",
        "    collaborative_recommendations = get_collaborative_filtering_recommendations(user_id, model, trainset, data, n)\n",
        "    if collaborative_recommendations:\n",
        "        selected_item = collaborative_recommendations[0]  # 첫 번째 협업 필터링 추천 항목 선택\n",
        "        content_based_recommendations = get_content_based_recommendations(selected_item, cosine_sim, data, n)\n",
        "    else:\n",
        "        content_based_recommendations = []\n",
        "\n",
        "    hybrid_recommendations = collaborative_recommendations + content_based_recommendations\n",
        "    return hybrid_recommendations\n",
        "\n",
        "# 예시: User_000001 사용자에게 하이브리드 추천\n",
        "user_id = 'User_000001'\n",
        "hybrid_recommendations = hybrid_recommendations(user_id, model, trainset, data, cosine_sim)\n",
        "print(\"Hybrid recommendations for\", user_id, \":\", hybrid_recommendations)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1EddF83OHid",
        "outputId": "48fe2365-6d4b-4ef5-b0c3-9f43130db423"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hybrid recommendations for User_000001 : ['raffle_002502', 'raffle_000974', 'raffle_008164', 'raffle_002819', 'raffle_005583', 'raffle_000452', 'raffle_000345', 'raffle_001693', 'raffle_004248', 'raffle_004794', 'raffle_000013', 'raffle_000016', 'raffle_000018', 'raffle_000020', 'raffle_000022', 'raffle_000033', 'raffle_000046', 'raffle_000057', 'raffle_000058', 'raffle_000062']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SKgerU2nPDYr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}