写一段代码，用java编写基于内容的推荐算法，推荐音乐家

好的，下面是一段简单的Java代码，用于实现基于内容的推荐算法，推荐音乐家：

import java.util.*;

public class ContentBasedRecommender {
    // 音乐家的特征数据
    private Map<String, Map<String, Double>> musicianData;

    public ContentBasedRecommender(Map<String, Map<String, Double>> musicianData) {
        this.musicianData = musicianData;
    }

    // 计算两个音乐家之间的余弦相似度
    private double cosineSimilarity(Map<String, Double> v1, Map<String, Double> v2) {
        double dotProduct = 0.0;
        double norm1 = 0.0;
        double norm2 = 0.0;
        for (String key : v1.keySet()) {
            if (v2.containsKey(key)) {
                dotProduct += v1.get(key) * v2.get(key);
            }
            norm1 += v1.get(key) * v1.get(key);
        }
        for (String key : v2.keySet()) {
            norm2 += v2.get(key) * v2.get(key);
        }
        if (norm1 == 0.0 || norm2 == 0.0) {
            return 0.0;
        } else {
            return dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));
        }
    }

    // 计算每个音乐家的推荐分数
    public List<Recommendation> getRecommendations(Map<String, Double> userLikes) {
        Map<String, Double> scores = new HashMap<>();
        Map<String, Double> totalSim = new HashMap<>();
        for (String musician : musicianData.keySet()) {
            double score = 0.0;
            double simSum = 0.0;
            for (String like : userLikes.keySet()) {
                if (musicianData.get(musician).containsKey(like)) {
                    double sim = cosineSimilarity(musicianData.get(musician), musicianData.get(like));
                    score += sim * userLikes.get(like);
                    simSum += sim;
                }
            }
            if (simSum > 0.0) {
                scores.put(musician, score);
                totalSim.put(musician, simSum);
            }
        }
        List<Recommendation> recommendations = new ArrayList<>();
        for (String musician : scores.keySet()) {
            double score = scores.get(musician);
            double simSum = totalSim.get(musician);
            double normScore = score / simSum;
            recommendations.add(new Recommendation(musician, normScore));
        }
        Collections.sort(recommendations);
        return recommendations;
    }

    public static void main(String[] args) {
        // 初始化音乐家的特征数据
        Map<String, Map<String, Double>> musicianData = new HashMap<>();
        Map<String, Double> taylorSwift = new HashMap<>();
        taylorSwift.put("Pop", 1.0);
        taylorSwift.put("Country", 0.5);
        taylorSwift.put("Folk", 0.2);
        musicianData.put("Taylor Swift", taylorSwift);
        Map<String, Double> beyonce = new HashMap<>();
        beyonce.put("R&B", 1.0);
        beyonce.put("Pop", 0.8);
        beyonce.put("Hip Hop", 0.5);
        musicianData.put("Beyonce", beyonce);
        Map<String, Double> justinBieber = new HashMap<>();
        justinBieber.put("Pop", 1.0);
        justinBieber.put("Dance", 0.8);
        justinBieber.put("R&B", 0.5);
        musicianData.put("Justin Bieber", justinBieber);

        // 初始化用户的历史喜好
        Map<String, Double> userLikes = new HashMap<>();
        userLikes.put("Pop", 1.0);
        userLikes.put("Country", 0.8);

        // 创建基于内容的推荐算法对象
        ContentBasedRecommender recommender = new ContentBasedRecommender(musicianData);

        // 获得推荐结果
        List<Recommendation> recommendations = recommender.getRecommendations(userLikes);

        // 输出推荐结果
        for (Recommendation recommendation : recommendations) {
            System.out.println(recommendation.getMusician() + " - " + recommendation.getScore());
        }
    }
}

class Recommendation implements Comparable<Recommendation> {
    private String musician;
    private double score;

    public Recommendation(String musician, double score) {
        this.musician = musician;
        this.score = score;
    }

    public String getMusician() {
        return musician;
    }

    public double getScore() {
        return score;
    }

    @Override
    public int compareTo(Recommendation other) {
        return Double.compare(other.score, score);
    }
}

这段代码实现了基于内容的推荐算法，推荐音乐家。其中，`ContentBasedRecommender`类表示推荐算法，`Recommendation`类表示推荐结果。在`main`方法中，初始化了音乐家的特征数据和用户的历史喜好，使用`ContentBasedRecommender`类获得推荐结果，并输出推荐结果。