一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
开发语言：Java 后端框架：Spring Boot(Spring+SpringMVC+Mybatis) 前端：Vue 数据库：MySQL 系统架构：B/S 开发工具：IDEA
《基于线性回归的音乐推荐系统》是一个采用Spring Boot框架构建的智能音乐推荐平台，通过集成线性回归算法实现个性化音乐推荐功能。系统采用前后端分离的B/S架构，前端使用Vue框架构建用户交互界面，后端基于Spring+SpringMVC+MyBatis技术栈提供数据服务，MySQL数据库存储用户信息、音乐资源及行为数据。系统核心功能涵盖用户账户管理、音乐分类体系构建、音乐库资源维护、音乐数据统计分析、网络音乐数据爬取以及基于线性回归模型的收藏数预测等模块。通过分析用户历史收藏行为、音乐特征属性等多维度数据，系统能够预测用户对特定音乐的收藏倾向，为用户提供精准的音乐推荐服务。整个系统在IDEA开发环境中完成开发，具备良好的可扩展性和维护性，为音乐爱好者提供个性化的音乐发现体验。
三、视频解说
四、部分功能展示
五、部分代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.ml.regression.LinearRegression;
import org.apache.spark.ml.regression.LinearRegressionModel;
import org.apache.spark.ml.feature.VectorAssembler;
@Service
public class MusicRecommendationService {
private SparkSession spark = SparkSession.builder().appName("MusicRecommendation").master("local").getOrCreate();
public PredictionResult predictCollectionCount(MusicPredictionRequest request) {
try {
Dataset<Row> musicData = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/music_db")
.option("dbtable", "music_statistics")
.option("user", "root")
.option("password", "password")
.load();
String[] featureColumns = {"play_count", "like_count", "share_count", "duration", "genre_score"};
VectorAssembler assembler = new VectorAssembler()
.setInputCols(featureColumns)
.setOutputCol("features");
Dataset<Row> assembledData = assembler.transform(musicData);
LinearRegression lr = new LinearRegression()
.setFeaturesCol("features")
.setLabelCol("collection_count")
.setMaxIter(100)
.setRegParam(0.01);
LinearRegressionModel model = lr.fit(assembledData);
Dataset<Row> newMusicData = spark.createDataFrame(Arrays.asList(
RowFactory.create(request.getPlayCount(), request.getLikeCount(),
request.getShareCount(), request.getDuration(), request.getGenreScore())
), assembledData.schema());
Dataset<Row> assembledNewData = assembler.transform(newMusicData);
Dataset<Row> predictions = model.transform(assembledNewData);
Row result = predictions.select("prediction").first();
double predictedCount = result.getDouble(0);
return new PredictionResult(Math.max(0, (int)Math.round(predictedCount)));
} catch (Exception e) {
logger.error("预测收藏数失败", e);
throw new ServiceException("收藏数预测服务异常");
}
}
public List<Music> recommendMusicByUser(Long userId) {
try {
User user = userMapper.selectById(userId);
if (user == null) {
throw new ServiceException("用户不存在");
}
List<UserBehavior> userBehaviors = behaviorMapper.selectByUserId(userId);
Map<String, Double> genrePreferences = calculateGenrePreferences(userBehaviors);
List<Music> candidateMusic = musicMapper.selectCandidateMusic(userId);
List<RecommendationScore> scoredMusic = new ArrayList<>();
for (Music music : candidateMusic) {
double genreScore = genrePreferences.getOrDefault(music.getGenre(), 0.0);
double popularityScore = Math.log(music.getPlayCount() + 1) * 0.3;
double freshnessScore = calculateFreshnessScore(music.getCreateTime());
double totalScore = genreScore * 0.5 + popularityScore * 0.3 + freshnessScore * 0.2;
scoredMusic.add(new RecommendationScore(music, totalScore));
}
scoredMusic.sort((a, b) -> Double.compare(b.getScore(), a.getScore()));
return scoredMusic.stream()
.limit(20)
.map(RecommendationScore::getMusic)
.collect(Collectors.toList());
} catch (Exception e) {
logger.error("音乐推荐失败，用户ID: {}", userId, e);
throw new ServiceException("音乐推荐服务异常");
}
}
public CrawlResult crawlMusicData(String sourceUrl, String category) {
try {
CloseableHttpClient httpClient = HttpClients.createDefault();
HttpGet request = new HttpGet(sourceUrl);
request.addHeader("User-Agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36");
CloseableHttpResponse response = httpClient.execute(request);
String html = EntityUtils.toString(response.getEntity(), StandardCharsets.UTF_8);
Document document = Jsoup.parse(html);
Elements musicElements = document.select(".music-item");
List<Music> crawledMusic = new ArrayList<>();
for (Element element : musicElements) {
try {
String title = element.select(".music-title").text();
String artist = element.select(".music-artist").text();
String album = element.select(".music-album").text();
String durationStr = element.select(".music-duration").text();
int duration = parseDuration(durationStr);
String playCountStr = element.select(".play-count").text();
int playCount = parsePlayCount(playCountStr);
if (StringUtils.isNotBlank(title) && StringUtils.isNotBlank(artist)) {
Music music = new Music();
music.setTitle(title);
music.setArtist(artist);
music.setAlbum(album);
music.setDuration(duration);
music.setPlayCount(playCount);
music.setGenre(category);
music.setCreateTime(new Date());
music.setUpdateTime(new Date());
crawledMusic.add(music);
}
} catch (Exception e) {
logger.warn("解析音乐数据失败", e);
}
}
int savedCount = 0;
for (Music music : crawledMusic) {
try {
if (musicMapper.selectByTitleAndArtist(music.getTitle(), music.getArtist()) == null) {
musicMapper.insert(music);
savedCount++;
}
} catch (Exception e) {
logger.warn("保存音乐数据失败: {}", music.getTitle(), e);
}
}
return new CrawlResult(crawledMusic.size(), savedCount);
} catch (Exception e) {
logger.error("音乐数据爬取失败，URL: {}", sourceUrl, e);
throw new ServiceException("音乐数据爬取异常");
}
}
}
六、部分文档展示
七、END
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