💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学习实战项目
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在线音乐个性化推荐APP的设计与实现介绍
《在线音乐个性化推荐APP的设计与实现》是一款基于SpringBoot后端框架和uni-app前端技术栈开发的智能音乐推荐应用系统。该系统采用C/S与B/S混合架构模式，通过MySQL数据库存储用户行为数据、歌曲信息、歌手资料等核心数据，实现了用户个人中心管理、音乐分类浏览、歌手信息查询、个性化歌单推荐、社交互动交流、论坛讨论、留言板管理等主要功能模块。系统支持微信小程序端和Android移动端双平台运行，用户可以根据个人喜好建立专属音乐档案，系统通过分析用户的听歌历史、收藏行为、评分数据等信息，运用协同过滤和内容推荐算法为用户提供精准的音乐推荐服务。平台还集成了敏感词过滤、举报处理、用户互动等社区管理功能，为用户打造一个安全、友好的音乐分享环境。整个系统界面简洁美观，操作流程清晰，能够满足不同用户群体对音乐发现、收藏、分享的个性化需求。
在线音乐个性化推荐APP的设计与实现演示视频
在线音乐个性化推荐APP的设计与实现演示图片
在线音乐个性化推荐APP的设计与实现代码展示
@Service
public class MusicRecommendationService {
@Autowired
private SparkSession spark = SparkSession.builder().appName("MusicRecommendation").master("local[*]").getOrCreate();
@Autowired
private UserBehaviorMapper userBehaviorMapper;
@Autowired
private SongMapper songMapper;
@Autowired
private PlaylistMapper playlistMapper;
public List<Song> getPersonalizedRecommendations(Long userId) {
Dataset<Row> userBehaviorData = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "user_behavior", connectionProperties);
Dataset<Row> songFeatureData = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "song_features", connectionProperties);
Dataset<Row> userSongMatrix = userBehaviorData.groupBy("user_id", "song_id").agg(avg("rating").as("avg_rating"));
Dataset<Row> userProfile = userSongMatrix.filter(col("user_id").equalTo(userId));
Dataset<Row> similarUsers = userSongMatrix.join(userProfile, "song_id").filter(col("user_id").notEqual(userId));
Dataset<Row> userSimilarityScores = similarUsers.groupBy("user_id").agg(corr("avg_rating", userProfile.col("avg_rating")).as("similarity_score"));
Dataset<Row> topSimilarUsers = userSimilarityScores.orderBy(desc("similarity_score")).limit(50);
Dataset<Row> candidateSongs = topSimilarUsers.join(userSongMatrix, "user_id").filter(col("avg_rating").gt(3.5));
Dataset<Row> songPopularityScores = candidateSongs.groupBy("song_id").agg(avg("avg_rating").as("predicted_rating"), count("*").as("popularity"));
Dataset<Row> finalRecommendations = songPopularityScores.join(songFeatureData, "song_id").orderBy(desc("predicted_rating"), desc("popularity")).limit(20);
List<Row> recommendationRows = finalRecommendations.collectAsList();
List<Song> recommendedSongs = new ArrayList<>();
for (Row row : recommendationRows) {
Song song = new Song();
song.setSongId(row.getLong("song_id"));
song.setSongName(row.getString("song_name"));
song.setArtistName(row.getString("artist_name"));
song.setPredictedRating(row.getDouble("predicted_rating"));
recommendedSongs.add(song);
}
return recommendedSongs;
}
public void recordUserBehavior(Long userId, Long songId, String actionType, Double rating) {
Dataset<Row> realtimeBehaviorData = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "user_behavior", connectionProperties);
Dataset<Row> newBehaviorRecord = spark.createDataFrame(Arrays.asList(RowFactory.create(userId, songId, actionType, rating, System.currentTimeMillis())), behaviorSchema);
Dataset<Row> updatedBehaviorData = realtimeBehaviorData.union(newBehaviorRecord);
Dataset<Row> userListeningHistory = updatedBehaviorData.filter(col("user_id").equalTo(userId)).orderBy(desc("timestamp"));
Dataset<Row> genrePreferences = userListeningHistory.join(spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "songs", connectionProperties), "song_id").groupBy("genre").agg(avg("rating").as("genre_preference"), count("*").as("listen_count"));
Dataset<Row> artistPreferences = userListeningHistory.join(spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "songs", connectionProperties), "song_id").groupBy("artist_id").agg(avg("rating").as("artist_preference"), count("*").as("listen_count"));
Dataset<Row> timeBasedPatterns = userListeningHistory.withColumn("hour_of_day", hour(from_unixtime(col("timestamp").divide(1000)))).groupBy("hour_of_day").agg(avg("rating").as("time_preference"));
genrePreferences.write().mode(SaveMode.Overwrite).jdbc("jdbc:mysql://localhost:3306/music_db", "user_genre_preferences", connectionProperties);
artistPreferences.write().mode(SaveMode.Overwrite).jdbc("jdbc:mysql://localhost:3306/music_db", "user_artist_preferences", connectionProperties);
timeBasedPatterns.write().mode(SaveMode.Overwrite).jdbc("jdbc:mysql://localhost:3306/music_db", "user_time_patterns", connectionProperties);
UserBehavior behaviorRecord = new UserBehavior();
behaviorRecord.setUserId(userId);
behaviorRecord.setSongId(songId);
behaviorRecord.setActionType(actionType);
behaviorRecord.setRating(rating);
behaviorRecord.setTimestamp(new Date());
userBehaviorMapper.insert(behaviorRecord);
}
public Playlist generateSmartPlaylist(Long userId, String moodTag, Integer playlistLength) {
Dataset<Row> userPreferenceData = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "user_genre_preferences", connectionProperties).filter(col("user_id").equalTo(userId));
Dataset<Row> moodBasedSongs = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "songs", connectionProperties).filter(col("mood_tags").contains(moodTag));
Dataset<Row> userFavoriteSongs = spark.read().jdbc("jdbc:mysql://localhost:3306/music_db", "user_behavior", connectionProperties).filter(col("user_id").equalTo(userId).and(col("rating").gt(4.0)));
Dataset<Row> candidatePlaylistSongs = moodBasedSongs.join(userPreferenceData, "genre").withColumn("preference_score", col("genre_preference").multiply(col("popularity_score")));
Dataset<Row> diversifiedSongs = candidatePlaylistSongs.withColumn("random_factor", rand()).orderBy(desc("preference_score"), desc("random_factor"));
Dataset<Row> finalPlaylistSongs = diversifiedSongs.limit(playlistLength);
List<Row> playlistSongRows = finalPlaylistSongs.collectAsList();
Playlist smartPlaylist = new Playlist();
smartPlaylist.setUserId(userId);
smartPlaylist.setPlaylistName("智能歌单-" + moodTag);
smartPlaylist.setMoodTag(moodTag);
smartPlaylist.setCreateTime(new Date());
smartPlaylist.setSongCount(playlistSongRows.size());
playlistMapper.insert(smartPlaylist);
List<PlaylistSong> playlistSongs = new ArrayList<>();
for (int i = 0; i < playlistSongRows.size(); i++) {
Row songRow = playlistSongRows.get(i);
PlaylistSong playlistSong = new PlaylistSong();
playlistSong.setPlaylistId(smartPlaylist.getPlaylistId());
playlistSong.setSongId(songRow.getLong("song_id"));
playlistSong.setSongOrder(i + 1);
playlistSong.setPreferenceScore(songRow.getDouble("preference_score"));
playlistSongs.add(playlistSong);
}
playlistMapper.batchInsertPlaylistSongs(playlistSongs);
return smartPlaylist;
}
}
在线音乐个性化推荐APP的设计与实现文档展示
💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学习实战项目
