基于内容推荐算法java代码

以下是一个简单的基于内容推荐算法的Java代码示例：

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class ContentBasedRecommendation {

    public static void main(String[] args) {
        // 创建用户喜好矩阵
        Map<String, Map<String, Integer>> userItemMatrix = new HashMap<>();
        Map<String, Integer> user1 = new HashMap<>();
        user1.put("item1", 5);
        user1.put("item2", 3);
        user1.put("item3", 2);
        user1.put("item4", 4);
        user1.put("item5", 1);
        userItemMatrix.put("user1", user1);
        Map<String, Integer> user2 = new HashMap<>();
        user2.put("item1", 2);
        user2.put("item2", 1);
        user2.put("item3", 4);
        user2.put("item4", 3);
        user2.put("item5", 5);
        userItemMatrix.put("user2", user2);
        Map<String, Integer> user3 = new HashMap<>();
        user3.put("item1", 3);
        user3.put("item2", 2);
        user3.put("item3", 5);
        user3.put("item4", 1);
        user3.put("item5", 4);
        userItemMatrix.put("user3", user3);

        // 创建物品特征矩阵
        Map<String, Map<String, Integer>> itemFeatureMatrix = new HashMap<>();
        Map<String, Integer> item1 = new HashMap<>();
        item1.put("feature1", 5);
        item1.put("feature2", 2);
        item1.put("feature3", 1);
        itemFeatureMatrix.put("item1", item1);
        Map<String, Integer> item2 = new HashMap<>();
        item2.put("feature1", 3);
        item2.put("feature2", 5);
        item2.put("feature3", 2);
        itemFeatureMatrix.put("item2", item2);
        Map<String, Integer> item3 = new HashMap<>();
        item3.put("feature1", 4);
        item3.put("feature2", 1);
        item3.put("feature3", 5);
        itemFeatureMatrix.put("item3", item3);
        Map<String, Integer> item4 = new HashMap<>();
        item4.put("feature1", 2);
        item4.put("feature2", 4);
        item4.put("feature3", 3);
        itemFeatureMatrix.put("item4", item4);
        Map<String, Integer> item5 = new HashMap<>();
        item5.put("feature1", 1);
        item5.put("feature2", 3);
        item5.put("feature3", 4);
        itemFeatureMatrix.put("item5", item5);

        // 对用户进行推荐
        String userId = "user1";
        List<String> recommendedItems = recommend(userItemMatrix, itemFeatureMatrix, userId);
        System.out.println("为用户 " + userId + " 推荐的物品列表：" + recommendedItems);
    }

    public static List<String> recommend(Map<String, Map<String, Integer>> userItemMatrix,
                                         Map<String, Map<String, Integer>> itemFeatureMatrix,
                                         String userId) {
        // 获取用户评分过的物品
        Map<String, Integer> userRatings = userItemMatrix.get(userId);

        // 计算用户的特征向量
        Map<String, Double> userVector = new HashMap<>();
        for (String feature : itemFeatureMatrix.get("item1").keySet()) {
            double sum = 0.0;
            for (String itemId : userRatings.keySet()) {
                int rating = userRatings.get(itemId);
                int featureValue = itemFeatureMatrix.get(itemId).getOrDefault(feature, 0);
                sum += rating * featureValue;
            }
            userVector.put(feature, sum);
        }

        // 计算物品与用户特征向量的相似度
        Map<String, Double> itemSimilarity = new HashMap<>();
        for (String itemId : itemFeatureMatrix.keySet()) {
            double dotProduct = 0.0;
            double itemNorm = 0.0;
            double userNorm = 0.0;
            for (String feature : itemFeatureMatrix.get(itemId).keySet()) {
                int itemValue = itemFeatureMatrix.get(itemId).get(feature);
                double userValue = userVector.getOrDefault(feature, 0.0);
                dotProduct += itemValue * userValue;
                itemNorm += itemValue * itemValue;
                userNorm += userValue * userValue;
            }
            itemSimilarity.put(itemId, dotProduct / Math.sqrt(itemNorm * userNorm));
        }

        // 对物品相似度进行排序，取前N个物品作为推荐结果
        List<String> recommendedItems = new ArrayList<>();
        itemSimilarity.entrySet().stream()
                .sorted(Map.Entry.<String, Double>comparingByValue().reversed())
                .forEach(entry -> {
                    if (!userRatings.containsKey(entry.getKey())) {
                        recommendedItems.add(entry.getKey());
                    }
                });
        return recommendedItems.subList(0, Math.min(recommendedItems.size(), 3));
    }

}

该代码实现了一个简单的基于内容推荐算法，使用了用户喜好矩阵和物品特征矩阵来计算物品之间的相似度，并根据相似度对物品进行排序，最后推荐相似度最高的前N个物品。请注意，该代码的实现仅供参考，实际应用中需要根据具体情况进行修改和优化。