Java使用协同过滤算法的代码示例

import java.util.*;

// 用户评分项
class Rating {
    int userId;
    int itemId;
    double rating;

    public Rating(int userId, int itemId, double rating) {
        this.userId = userId;
        this.itemId = itemId;
        this.rating = rating;
    }
}

// 用户评分数据存储
class UserRatings {
    Map<Integer, Double> ratings = new HashMap<>();

    public void addRating(int itemId, double rating) {
        ratings.put(itemId, rating);
    }

    public double getRating(int itemId) {
        return ratings.getOrDefault(itemId, 0.0);
    }

    // 省略其他方法...
}

// 数据存储类（简化的示例）
class DataModel {
    Map<Integer, UserRatings> userRatings = new HashMap<>();

    public void addRating(Rating rating) {
        UserRatings userRating = userRatings.getOrDefault(rating.userId, new UserRatings());
        userRating.addRating(rating.itemId, rating.rating);
        userRatings.put(rating.userId, userRating);
    }

    // 省略其他方法...
}

public class UserBasedCollaborativeFiltering {

    // 计算两个用户之间的皮尔逊相关系数
    public static double pearsonCorrelation(UserRatings user1, UserRatings user2) {
        // ... 这里省略了计算皮尔逊相关系数的具体实现 ...
        // 需要考虑共同评分项、评分均值等
        return 0.0; // 示例返回0，实际应计算皮尔逊相关系数
    }

    // 为指定用户生成推荐
    public static List<Integer> generateRecommendations(DataModel dataModel, int targetUserId, int numRecommendations) {
        List<Integer> recommendations = new ArrayList<>();

        // 获取目标用户的评分数据
        UserRatings targetUserRatings = dataModel.userRatings.get(targetUserId);
        if (targetUserRatings == null) {
            return recommendations; // 如果没有评分数据，则无法推荐
        }

        // 计算目标用户与其他用户的相似度
        Map<Integer, Double> userSimilarities = new HashMap<>();
        for (int userId : dataModel.userRatings.keySet()) {
            if (userId != targetUserId) {
                UserRatings otherUserRatings = dataModel.userRatings.get(userId);
                double similarity = pearsonCorrelation(targetUserRatings, otherUserRatings);
                if (similarity > 0) { // 只考虑相似度大于0的用户
                    userSimilarities.put(userId, similarity);
                }
            }
        }

        // 根据相似用户的评分生成推荐
        // ... 这里省略了根据相似度生成推荐的具体实现 ...
        // 需要遍历相似用户评过分的项目，并考虑相似度进行排序

        // 示例：随机添加一些推荐ID
        for (int i = 0; i < numRecommendations && i < 100; i++) { // 假设最多推荐100首
            recommendations.add(i + 1000); // 示例ID，实际应用中应基于相似用户评分计算
        }

        return recommendations;
    }

    // 主函数示例
    public static void main(String[] args) {
        DataModel dataModel = new DataModel();
        // ... 加载或添加评分数据到dataModel中 ...

        // 为用户ID为1的用户生成5个推荐
        List<Integer> recommendations = generateRecommendations(dataModel, 1, 5);
        for (int itemId : recommendations) {
            System.out.println("推荐ID: " + itemId);
        }
    }
}

Map<String, Map<String, Double>> userRatings = new HashMap<>();
// 假设我们有两个用户和三个物品
userRatings.put("user1", Map.of("item1", 5.0, "item2", 3.0, "item3", 4.0));
userRatings.put("user2", Map.of("item1", 4.0, "item2", 4.5, "item3", 2.0));
// ... 为其他用户添加评分

public double cosineSimilarity(Map<String, Double> ratings1, Map<String, Double> ratings2) {
    double dotProduct = 0.0;
    double norm1 = 0.0;
    double norm2 = 0.0;

    // 找出两个用户共同评分的物品
    Set<String> commonItems = new HashSet<>(ratings1.keySet());
    commonItems.retainAll(ratings2.keySet());

    for (String item : commonItems) {
        dotProduct += ratings1.get(item) * ratings2.get(item);
        norm1 += Math.pow(ratings1.get(item), 2);
        norm2 += Math.pow(ratings2.get(item), 2);
    }

    if (norm1 == 0 || norm2 == 0) {
        return 0.0; // 如果没有共同评分的物品，返回0
    }

    double similarity = dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));
    return similarity;
}