Java 实现协同过滤算法推荐算法的示例代码

import java.util.HashMap;
import java.util.Map;
public class CollaborativeFiltering {
    // 用户评分矩阵
    private static final Map<String, Map<String, Integer>> ratings = new HashMap<>();
    static {
        ratings.put("UserA", Map.of("Item1", 5, "Item2", 3, "Item4", 1));
        ratings.put("UserB", Map.of("Item1", 4, "Item4", 1));
        ratings.put("UserC", Map.of("Item2", 1, "Item4", 5));
        ratings.put("UserD", Map.of("Item3", 5, "Item4", 4));
    }
    // 计算余弦相似度
    private double cosineSimilarity(Map<String, Integer> ratings1, Map<String, Integer> ratings2) {
        double dotProduct = 0.0;
        double normA = 0.0;
        double normB = 0.0;
        for (String item : ratings1.keySet()) {
            if (ratings2.containsKey(item)) {
                dotProduct += ratings1.get(item) * ratings2.get(item);
            }
            normA += Math.pow(ratings1.get(item), 2);
        }
        for (double rating : ratings2.values()) {
            normB += Math.pow(rating, 2);
        }
        normA = Math.sqrt(normA);
        normB = Math.sqrt(normB);
        return (normA == 0 || normB == 0) ? 0 : dotProduct / (normA * normB);
    }
    // 为用户推荐物品
    public Map<String, Double> recommendItems(String user) {
        Map<String, Integer> userRatings = ratings.get(user);
        Map<String, Double> scoreMap = new HashMap<>();
        for (String otherUser : ratings.keySet()) {
            if (!otherUser.equals(user)) {
                double similarity = cosineSimilarity(userRatings, ratings.get(otherUser));
                for (String item : ratings.get(otherUser).keySet()) {
                    if (!userRatings.containsKey(item)) {
                        scoreMap.put(item, scoreMap.getOrDefault(item, 0.0) + similarity * ratings.get(otherUser).get(item));
                    }
                }
            }
        }
        return scoreMap;
    }
    public static void main(String[] args) {
        CollaborativeFiltering cf = new CollaborativeFiltering();
        Map<String, Double> recommendations = cf.recommendItems("UserA");
        System.out.println("推荐物品给 UserA: " + recommendations);
    }
}

import java.util.HashMap;
import java.util.Map;
public class ItemBasedCollaborativeFiltering {
    private static final Map<String, Map<String, Integer>> ratings = new HashMap<>();
    static {
        ratings.put("UserA", Map.of("Item1", 5, "Item2", 3, "Item4", 1));
        ratings.put("UserB", Map.of("Item1", 4, "Item4", 1));
        ratings.put("UserC", Map.of("Item2", 1, "Item4", 5));
        ratings.put("UserD", Map.of("Item3", 5, "Item4", 4));
    }
    // 计算物品之间的余弦相似度
    private double cosineSimilarity(Map<String, Integer> item1, Map<String, Integer> item2) {
        // 与用户的计算相似
        // 省略相似度计算的具体实现
        return 0.0; // 这里应返回实际计算的相似度
    }
    // 为用户推荐物品
    public Map<String, Double> recommendItems(String user) {
        Map<String, Integer> userRatings = ratings.get(user);
        Map<String, Double> scoreMap = new HashMap<>();
        // 计算物品之间的相似度
        // 省略物品相似度计算和推荐逻辑的实现
        return scoreMap;
    }
    public static void main(String[] args) {
        ItemBasedCollaborativeFiltering ibcf = new ItemBasedCollaborativeFiltering();
        Map<String, Double> recommendations = ibcf.recommendItems("UserA");
        System.out.println("推荐物品给 UserA: " + recommendations);
    }
}