用java编写一段协同过滤算法推荐音乐家

以下是一个简单的基于Java的协同过滤算法推荐音乐家的示例代码：

import java.util.*;

public class MusicRecommendation {
    private static final int TOP_N = 5; // 推荐的音乐家数量
    private static final int NEIGHBORS_SIZE = 3; // 最邻近的用户数量
    private static final double SIMILARITY_THRESHOLD = 0.5; // 最小相似度阈值

    // 用户-音乐家矩阵
    private static final double[][] RATINGS = {
            {5.0, 3.0, 4.0, 4.0, Double.NaN, 1.0},
            {4.0, Double.NaN, 4.0, 5.0, 3.0, 2.0},
            {Double.NaN, 4.0, 5.0, 3.0, 4.0, 4.0},
            {2.0, 3.0, Double.NaN, 4.0, 3.0, 4.0},
            {3.0, 4.0, 5.0, Double.NaN, 5.0, Double.NaN},
            {4.0, 3.0, 3.0, 5.0, 4.0, 5.0}
    };

    // 用户相似度矩阵
    private static double[][] similarities = new double[RATINGS.length][RATINGS.length];

    public static void main(String[] args) {
        // 计算用户相似度
        for (int i = 0; i < similarities.length; i++) {
            for (int j = 0; j < similarities.length; j++) {
                if (i == j) {
                    similarities[i][j] = 1.0;
                } else {
                    similarities[i][j] = calcSimilarity(RATINGS[i], RATINGS[j]);
                }
            }
        }

        // 推荐音乐家
        double[] userRatings = {4.0, Double.NaN, Double.NaN, 5.0, 3.0, 4.0}; // 用户对音乐家的评分
        List<Integer> topN = recommend(userRatings);
        System.out.println("推荐的音乐家为：" + topN);
    }

    // 计算用户之间的相似度
    private static double calcSimilarity(double[] ratings1, double[] ratings2) {
        double numerator = 0.0, denominator1 = 0.0, denominator2 = 0.0;
        for (int i = 0; i < ratings1.length; i++) {
            if (!Double.isNaN(ratings1[i]) && !Double.isNaN(ratings2[i])) {
                numerator += ratings1[i] * ratings2[i];
                denominator1 += Math.pow(ratings1[i], 2);
                denominator2 += Math.pow(ratings2[i], 2);
            }
        }
        double denominator = Math.sqrt(denominator1) * Math.sqrt(denominator2);
        return denominator == 0.0 ? 0.0 : numerator / denominator;
    }

    // 推荐音乐家
    private static List<Integer> recommend(double[] userRatings) {
        // 找到最邻近的用户
        List<Integer> neighbors = new ArrayList<>();
        for (int i = 0; i < similarities.length; i++) {
            if (i != userRatings.length - 1 && similarities[userRatings.length - 1][i] >= SIMILARITY_THRESHOLD) {
                neighbors.add(i);
            }
        }
        if (neighbors.size() == 0) {
            return Collections.emptyList();
        }

        // 计算音乐家的推荐指数
        Map<Integer, Double> scores = new HashMap<>();
        for (int i = 0; i < RATINGS[userRatings.length - 1].length; i++) {
            if (Double.isNaN(userRatings[i])) {
                double numerator = 0.0, denominator = 0.0;
                for (int neighbor : neighbors) {
                    if (!Double.isNaN(RATINGS[neighbor][i])) {
                        numerator += similarities[userRatings.length - 1][neighbor] * RATINGS[neighbor][i];
                        denominator += similarities[userRatings.length - 1][neighbor];
                    }
                }
                double score = denominator == 0.0 ? 0.0 : numerator / denominator;
                scores.put(i, score);
            }
        }

        // 返回得分最高的音乐家
        List<Integer> topN = new ArrayList<>();
        for (int i = 0; i < TOP_N; i++) {
            double maxScore = Double.NEGATIVE_INFINITY;
            int maxIndex = -1;
            for (int index : scores.keySet()) {
                if (scores.get(index) > maxScore) {
                    maxScore = scores.get(index);
                    maxIndex = index;
                }
            }
            if (maxIndex != -1) {
                topN.add(maxIndex);
                scores.remove(maxIndex);
            }
        }
        return topN;
    }
}

该算法使用了一个用户-音乐家矩阵来存储用户对音乐家的评分，对于未评分的音乐家，使用协同过滤算法来预测用户的评分，并返回得分最高的音乐家作为推荐结果。