ChatGPT Java: 개인화된 추천 시스템을 구축하는 방법

import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.PreparedStatement;
import java.sql.SQLException;

public class DataCollector {
    private static final String JDBC_URL = "jdbc:mysql://localhost:3306/recommendation_system";
    private static final String USERNAME = "root";
    private static final String PASSWORD = "password";

    public static void main(String[] args) {
        try(Connection connection = DriverManager.getConnection(JDBC_URL, USERNAME, PASSWORD)) {
            String sql = "INSERT INTO user_browsing_history (user_id, item_id, timestamp) VALUES (?, ?, ?)";
            PreparedStatement statement = connection.prepareStatement(sql);

            // 假设有一个用户浏览了商品1和商品2
            statement.setInt(1, 1); // 用户ID
            statement.setInt(2, 1); // 商品ID
            statement.setTimestamp(3, new java.sql.Timestamp(System.currentTimeMillis())); // 事件时间戳
            statement.executeUpdate();

            statement.setInt(1, 1);
            statement.setInt(2, 2);
            statement.setTimestamp(3, new java.sql.Timestamp(System.currentTimeMillis()));
            statement.executeUpdate();
        } catch (SQLException e) {
            e.printStackTrace();
        }
    }
}

import java.util.HashMap;
import java.util.Map;

public class SimilarityCalculator {
    public static void main(String[] args) {
        // 假设有两位用户
        Map<Integer, Map<Integer, Integer>> userItems = new HashMap<>();
        userItems.put(1, new HashMap<>());
        userItems.get(1).put(1, 5); // 用户1对商品1的评分是5
        userItems.get(1).put(2, 3); // 用户1对商品2的评分是3

        userItems.put(2, new HashMap<>());
        userItems.get(2).put(1, 4); // 用户2对商品1的评分是4
        userItems.get(2).put(2, 2); // 用户2对商品2的评分是2

        int userId1 = 1;
        int userId2 = 2;

        double similarity = calculateCosineSimilarity(userItems.get(userId1), userItems.get(userId2));
        System.out.println("用户1和用户2的相似度为：" + similarity);
    }

    private static double calculateCosineSimilarity(Map<Integer, Integer> user1, Map<Integer, Integer> user2) {
        double dotProduct = 0.0;
        double normUser1 = 0.0;
        double normUser2 = 0.0;

        for (Integer itemId : user1.keySet()) {
            if (user2.containsKey(itemId)) {
                dotProduct += user1.get(itemId) * user2.get(itemId);
            }
            normUser1 += Math.pow(user1.get(itemId), 2);
        }

        for (Integer itemId : user2.keySet()) {
            normUser2 += Math.pow(user2.get(itemId), 2);
        }

        return dotProduct / (Math.sqrt(normUser1) * Math.sqrt(normUser2));
    }
}

import java.util.*;

public class RecommendationEngine {
    public static void main(String[] args) {
        // 假设有3位用户
        Map<Integer, Map<Integer, Integer>> userItems = new HashMap<>();
        userItems.put(1, new HashMap<>());
        userItems.get(1).put(1, 5); // 用户1对商品1的评分是5
        userItems.get(1).put(2, 3); // 用户1对商品2的评分是3
        userItems.get(1).put(3, 4); // 用户1对商品3的评分是4

        userItems.put(2, new HashMap<>());
        userItems.get(2).put(1, 4); // 用户2对商品1的评分是4
        userItems.get(2).put(3, 2); // 用户2对商品3的评分是2

        userItems.put(3, new HashMap<>());
        userItems.get(3).put(2, 5); // 用户3对商品2的评分是5
        userItems.get(3).put(3, 2); // 用户3对商品3的评分是2

        int targetUserId = 1;

        Map<Integer, Double> recommendItems = generateRecommendations(userItems, targetUserId);
        System.out.println("为用户1生成的推荐结果为：" + recommendItems);
    }

    private static Map<Integer, Double> generateRecommendations(Map<Integer, Map<Integer, Integer>> userItems, int targetUserId) {
        Map<Integer, Double> recommendItems = new HashMap<>();
        Map<Integer, Integer> targetUserItems = userItems.get(targetUserId);

        for (Integer userId : userItems.keySet()) {
            if (userId != targetUserId) {
                Map<Integer, Integer> otherUserItems = userItems.get(userId);
                double similarity = calculateCosineSimilarity(targetUserItems, otherUserItems);

                for (Integer itemId : otherUserItems.keySet()) {
                    if (!targetUserItems.containsKey(itemId)) {
                        double rating = otherUserItems.get(itemId);
                        double weightedRating = rating * similarity;
                        recommendItems.put(itemId, recommendItems.getOrDefault(itemId, 0.0) + weightedRating);
                    }
                }
            }
        }

        return recommendItems;
    }

    private static double calculateCosineSimilarity(Map<Integer, Integer> user1, Map<Integer, Integer> user2) {
      // 略，与上一个代码示例中的calculateCosineSimilarity()方法相同
    }
}