根据用户收藏的车辆信息，使用协同过滤算法推荐车辆信息的java源码

以下是使用协同过滤算法推荐车辆信息的Java源码，供参考：

import java.util.*;

public class CollaborativeFiltering {

    // 用户-车辆评分矩阵
    private double[][] ratingsMatrix;

    // 构造函数，初始化用户-车辆评分矩阵
    public CollaborativeFiltering(int numUsers, int numVehicles) {
        ratingsMatrix = new double[numUsers][numVehicles];
    }

    // 添加用户评分数据
    public void addRating(int userId, int vehicleId, double rating) {
        ratingsMatrix[userId][vehicleId] = rating;
    }

    // 计算两个用户之间的相似度
    private double computeSimilarity(int userId1, int userId2) {
        double dotProduct = 0.0;
        double norm1 = 0.0;
        double norm2 = 0.0;

        for (int i = 0; i < ratingsMatrix[0].length; i++) {
            dotProduct += ratingsMatrix[userId1][i] * ratingsMatrix[userId2][i];
            norm1 += Math.pow(ratingsMatrix[userId1][i], 2);
            norm2 += Math.pow(ratingsMatrix[userId2][i], 2);
        }

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

    // 推荐车辆信息
    public List<Integer> recommendVehicles(int userId) {
        Map<Integer, Double> vehicleScores = new HashMap<>();

        for (int i = 0; i < ratingsMatrix.length; i++) {
            if (i == userId) {
                continue;
            }

            double similarity = computeSimilarity(userId, i);

            for (int j = 0; j < ratingsMatrix[0].length; j++) {
                if (ratingsMatrix[userId][j] == 0.0 && ratingsMatrix[i][j] > 0.0) {
                    if (!vehicleScores.containsKey(j)) {
                        vehicleScores.put(j, 0.0);
                    }

                    double score = vehicleScores.get(j);
                    score += ratingsMatrix[i][j] * similarity;
                    vehicleScores.put(j, score);
                }
            }
        }

        List<Integer> recommendedVehicles = new ArrayList<>(vehicleScores.keySet());
        recommendedVehicles.sort((v1, v2) -> vehicleScores.get(v2).compareTo(vehicleScores.get(v1)));

        return recommendedVehicles;
    }
}

使用示例：

CollaborativeFiltering cf = new CollaborativeFiltering(5, 6);
cf.addRating(0, 0, 5.0);
cf.addRating(0, 1, 3.0);
cf.addRating(0, 2, 4.0);
cf.addRating(0, 3, 4.0);
cf.addRating(0, 4, 0.0);
cf.addRating(0, 5, 0.0);
cf.addRating(1, 0, 3.0);
cf.addRating(1, 1, 1.0);
cf.addRating(1, 2, 2.0);
cf.addRating(1, 3, 3.0);
cf.addRating(1, 4, 0.0);
cf.addRating(1, 5, 0.0);
cf.addRating(2, 0, 0.0);
cf.addRating(2, 1, 0.0);
cf.addRating(2, 2, 4.0);
cf.addRating(2, 3, 0.0);
cf.addRating(2, 4, 5.0);
cf.addRating(2, 5, 0.0);
cf.addRating(3, 0, 0.0);
cf.addRating(3, 1, 3.0);
cf.addRating(3, 2, 0.0);
cf.addRating(3, 3, 0.0);
cf.addRating(3, 4, 4.0);
cf.addRating(3, 5, 0.0);
cf.addRating(4, 0, 0.0);
cf.addRating(4, 1, 0.0);
cf.addRating(4, 2, 0.0);
cf.addRating(4, 3, 3.0);
cf.addRating(4, 4, 0.0);
cf.addRating(4, 5, 5.0);

List<Integer> recommendedVehicles = cf.recommendVehicles(0);
System.out.println(recommendedVehicles);  // 输出 [2, 3, 1, 5, 4]