java通过用户使用协同过滤算法推荐商品代码怎样实现

以下是一个简单的Java实现协同过滤推荐算法的示例代码：

public class UserCF {
    private Map<Integer, Map<Integer, Double>> userItemRatingMatrix;

    public UserCF(Map<Integer, Map<Integer, Double>> userItemRatingMatrix) {
        this.userItemRatingMatrix = userItemRatingMatrix;
    }

    public Map<Integer, Double> recommendItems(int userId, int neighborNum, int recommendNum) {
        // 所有用户的相似度列表
        Map<Integer, Double> simMap = calcAllSim(userId);

        // 前k个相似用户的id
        List<Integer> neigborIds = findTopK(simMap, neighborNum);

        // 前k个相似用户对于商品的评分加权平均
        Map<Integer, Double> itemScoreMap = new HashMap<>();
        for (int neighborId : neigborIds) {
            Map<Integer, Double> itemRatingMap = userItemRatingMatrix.get(neighborId);
            for (int itemId : itemRatingMap.keySet()) {
                if (!userItemRatingMatrix.get(userId).containsKey(itemId)) {
                    itemScoreMap.merge(itemId, itemRatingMap.get(itemId) * simMap.get(neighborId), Double::sum);
                }
            }
        }

        // 推荐前n个商品
        List<Map.Entry<Integer, Double>> itemList = new ArrayList<>(itemScoreMap.entrySet());
        itemList.sort((o1, o2) -> o2.getValue().compareTo(o1.getValue()));
        Map<Integer, Double> recommendMap = new LinkedHashMap<>();
        int i = 0;
        for (Map.Entry<Integer, Double> entry : itemList) {
            recommendMap.put(entry.getKey(), entry.getValue());
            i++;
            if (i >= recommendNum) {
                break;
            }
        }

        return recommendMap;
    }

    private double calcSim(int userId1, int userId2) {
        Map<Integer, Double> ratings1 = userItemRatingMatrix.get(userId1);
        Map<Integer, Double> ratings2 = userItemRatingMatrix.get(userId2);

        double sumOfSquares1 = 0.0, sumOfSquares2 = 0.0, sumOfProducts = 0.0;

        for (int itemId : ratings1.keySet()) {
            if (ratings2.containsKey(itemId)) {
                double rating1 = ratings1.get(itemId), rating2 = ratings2.get(itemId);
                sumOfSquares1 += rating1 * rating1;
                sumOfSquares2 += rating2 * rating2;
                sumOfProducts += rating1 * rating2;
            }
        }

        if (sumOfProducts == 0.0) {
            return 0.0;
        } else {
            return sumOfProducts / Math.sqrt(sumOfSquares1 * sumOfSquares2);
        }
    }

    private Map<Integer, Double> calcAllSim(int userId) {
        Map<Integer, Double> simMap = new HashMap<>();
        Map<Integer, Double> ratings = userItemRatingMatrix.get(userId);

        for (int neighborId : userItemRatingMatrix.keySet()) {
            if (neighborId != userId) {
                simMap.put(neighborId, calcSim(userId, neighborId));
            }
        }

        return simMap;
    }

    private List<Integer> findTopK(Map<Integer, Double> simMap, int k) {
        List<Map.Entry<Integer, Double>> simList = new ArrayList<>(simMap.entrySet());
        simList.sort((o1, o2) -> o2.getValue().compareTo(o1.getValue()));
        List<Integer> topK = new ArrayList<>();
        int i = 0;
        for (Map.Entry<Integer, Double> entry : simList) {
            topK.add(entry.getKey());
            i++;
            if (i >= k) {
                break;
            }
        }
        return topK;
    }
}

以上代码基于用户对商品的评分矩阵实现，推荐算法主要分为三步：

1. 计算用户之间的相似度
2. 找到和目标用户最相近的k个用户
3. 对这k个用户评价过但目标用户没评价过的商品进行评分加权平均，得到推荐商品列表