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网站免费申请建站,今日国内新闻热点,广元做网站站排名,群晖做网站服务器下面我将为你实现一个基于物品的协同过滤推荐算法，适用于Java电商平台。这个实现包括核心算法、相似度计算和推荐生成。 1. 数据模型首先定义我们需要的数据模型： public class Item {private String itemId; // 商品IDprivate String name; …

下面我将为你实现一个基于物品的协同过滤推荐算法，适用于Java电商平台。这个实现包括核心算法、相似度计算和推荐生成。

1. 数据模型

首先定义我们需要的数据模型：

public class Item {private String itemId;    // 商品IDprivate String name;      // 商品名称// 其他商品属性...// 构造方法、getter和setter
}public class User {private String userId;    // 用户IDprivate String username; // 用户名// 其他用户属性...// 构造方法、getter和setter
}public class UserBehavior {private String userId;    // 用户IDprivate String itemId;    // 商品IDprivate double preference; // 偏好分数（如评分、购买次数等）private long timestamp;   // 行为时间戳// 构造方法、getter和setter
}

2. 相似度计算

基于物品的协同过滤核心是计算物品之间的相似度：

public class ItemSimilarity {/*** 计算物品之间的余弦相似度* @param itemPrefMap 物品-用户偏好矩阵: Map<itemId, Map<userId, preference>>* @return 物品相似度矩阵: Map<itemId, Map<itemId, similarity>>*/public Map<String, Map<String, Double>> calculateCosineSimilarity(Map<String, Map<String, Double>> itemPrefMap) {Map<String, Map<String, Double>> similarityMatrix = new HashMap<>();// 获取所有物品列表List<String> itemIds = new ArrayList<>(itemPrefMap.keySet());// 计算每对物品之间的相似度for (int i = 0; i < itemIds.size(); i++) {String itemId1 = itemIds.get(i);Map<String, Double> userPrefs1 = itemPrefMap.get(itemId1);// 初始化相似度矩阵similarityMatrix.put(itemId1, new HashMap<>());for (int j = i; j < itemIds.size(); j++) {String itemId2 = itemIds.get(j);if (itemId1.equals(itemId2)) {// 相同物品相似度为1similarityMatrix.get(itemId1).put(itemId2, 1.0);continue;}Map<String, Double> userPrefs2 = itemPrefMap.get(itemId2);// 计算两个物品的共同用户Set<String> commonUsers = new HashSet<>(userPrefs1.keySet());commonUsers.retainAll(userPrefs2.keySet());if (commonUsers.isEmpty()) {// 没有共同用户，相似度为0similarityMatrix.get(itemId1).put(itemId2, 0.0);similarityMatrix.get(itemId2).put(itemId1, 0.0);continue;}// 计算余弦相似度的分子和分母double dotProduct = 0.0;double norm1 = 0.0;double norm2 = 0.0;for (String userId : commonUsers) {double pref1 = userPrefs1.get(userId);double pref2 = userPrefs2.get(userId);dotProduct += pref1 * pref2;norm1 += Math.pow(pref1, 2);norm2 += Math.pow(pref2, 2);}// 计算余弦相似度double similarity = dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));// 对称矩阵，所以两个方向都存储similarityMatrix.get(itemId1).put(itemId2, similarity);// 确保对称矩阵的另一半也被填充if (!similarityMatrix.containsKey(itemId2)) {similarityMatrix.put(itemId2, new HashMap<>());}similarityMatrix.get(itemId2).put(itemId1, similarity);}}return similarityMatrix;}/*** 计算物品之间的改进余弦相似度（考虑用户平均评分）*/public Map<String, Map<String, Double>> calculateAdjustedCosineSimilarity(Map<String, Map<String, Double>> itemPrefMap,Map<String, Double> userAvgPrefMap) {// 实现类似上面，但在计算时减去用户平均评分// ...return similarityMatrix;}
}

3. 推荐引擎

基于物品相似度生成推荐：

public class ItemBasedRecommender {private Map<String, Map<String, Double>> itemSimilarityMatrix;private Map<String, Map<String, Double>> userItemPrefMap; // 用户-物品偏好矩阵public ItemBasedRecommender(Map<String, Map<String, Double>> itemSimilarityMatrix,Map<String, Map<String, Double>> userItemPrefMap) {this.itemSimilarityMatrix = itemSimilarityMatrix;this.userItemPrefMap = userItemPrefMap;}/*** 为用户推荐物品* @param userId 用户ID* @param topN 推荐数量* @return 推荐物品ID列表，按推荐分数降序排列*/public List<String> recommendItems(String userId, int topN) {// 获取用户历史行为物品Map<String, Double> userHistory = userItemPrefMap.getOrDefault(userId, new HashMap<>());// 存储物品的推荐分数Map<String, Double> recommendationScores = new HashMap<>();// 遍历用户历史行为物品for (Map.Entry<String, Double> entry : userHistory.entrySet()) {String historyItemId = entry.getKey();double historyPref = entry.getValue();// 获取与历史物品相似的物品Map<String, Double> similarItems = itemSimilarityMatrix.getOrDefault(historyItemId, new HashMap<>());// 计算推荐分数for (Map.Entry<String, Double> simEntry : similarItems.entrySet()) {String candidateItemId = simEntry.getKey();double similarity = simEntry.getValue();// 排除用户已经有过行为的物品if (!userHistory.containsKey(candidateItemId)) {double score = recommendationScores.getOrDefault(candidateItemId, 0.0);score += similarity * historyPref;recommendationScores.put(candidateItemId, score);}}}// 按推荐分数排序并返回topNreturn recommendationScores.entrySet().stream().sorted(Map.Entry.<String, Double>comparingByValue().reversed()).limit(topN).map(Map.Entry::getKey).collect(Collectors.toList());}
}

4. 数据预处理

在实际应用中，我们需要将原始用户行为数据转换为算法需要的格式：

public class DataPreprocessor {/*** 将用户行为列表转换为物品-用户偏好矩阵*/public Map<String, Map<String, Double>> convertToItemUserMatrix(List<UserBehavior> behaviors) {Map<String, Map<String, Double>> itemUserMatrix = new HashMap<>();for (UserBehavior behavior : behaviors) {String itemId = behavior.getItemId();String userId = behavior.getUserId();double preference = behavior.getPreference();itemUserMatrix.putIfAbsent(itemId, new HashMap<>());itemUserMatrix.get(itemId).put(userId, preference);}return itemUserMatrix;}/*** 将用户行为列表转换为用户-物品偏好矩阵*/public Map<String, Map<String, Double>> convertToUserItemMatrix(List<UserBehavior> behaviors) {Map<String, Map<String, Double>> userItemMatrix = new HashMap<>();for (UserBehavior behavior : behaviors) {String userId = behavior.getUserId();String itemId = behavior.getItemId();double preference = behavior.getPreference();userItemMatrix.putIfAbsent(userId, new HashMap<>());userItemMatrix.get(userId).put(itemId, preference);}return userItemMatrix;}/*** 计算每个用户的平均评分*/public Map<String, Double> calculateUserAveragePreference(List<UserBehavior> behaviors) {Map<String, Double> sumMap = new HashMap<>();Map<String, Integer> countMap = new HashMap<>();for (UserBehavior behavior : behaviors) {String userId = behavior.getUserId();double preference = behavior.getPreference();sumMap.put(userId, sumMap.getOrDefault(userId, 0.0) + preference);countMap.put(userId, countMap.getOrDefault(userId, 0) + 1);}Map<String, Double> avgMap = new HashMap<>();for (String userId : sumMap.keySet()) {avgMap.put(userId, sumMap.get(userId) / countMap.get(userId));}return avgMap;}
}

5. 完整使用示例

public class RecommendationDemo {public static void main(String[] args) {// 1. 模拟数据List<UserBehavior> behaviors = new ArrayList<>();behaviors.add(new UserBehavior("u1", "i1", 5.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u1", "i2", 3.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u1", "i3", 4.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u2", "i1", 4.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u2", "i3", 3.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u2", "i4", 4.5, System.currentTimeMillis()));behaviors.add(new UserBehavior("u3", "i2", 2.5, System.currentTimeMillis()));behaviors.add(new UserBehavior("u3", "i4", 4.0, System.currentTimeMillis()));behaviors.add(new UserBehavior("u3", "i5", 3.5, System.currentTimeMillis()));// 2. 数据预处理DataPreprocessor preprocessor = new DataPreprocessor();Map<String, Map<String, Double>> itemUserMatrix = preprocessor.convertToItemUserMatrix(behaviors);Map<String, Map<String, Double>> userItemMatrix = preprocessor.convertToUserItemMatrix(behaviors);Map<String, Double> userAvgPrefMap = preprocessor.calculateUserAveragePreference(behaviors);// 3. 计算物品相似度ItemSimilarity itemSimilarity = new ItemSimilarity();Map<String, Map<String, Double>> similarityMatrix = itemSimilarity.calculateCosineSimilarity(itemUserMatrix);// 4. 创建推荐引擎ItemBasedRecommender recommender = new ItemBasedRecommender(similarityMatrix, userItemMatrix);// 5. 为用户生成推荐String targetUserId = "u1";List<String> recommendations = recommender.recommendItems(targetUserId, 3);System.out.println("为用户 " + targetUserId + " 推荐的物品:");recommendations.forEach(System.out::println);}
}