💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
音乐推荐系统介绍
音乐推荐系统是一个基于大数据技术构建的智能化音乐服务平台，采用Hadoop+Spark分布式计算框架作为核心技术架构，结合Python开发语言和Django后端框架，前端使用Vue+ElementUI+Echarts技术栈实现用户界面设计和数据可视化展示。系统通过Spark SQL和Pandas、NumPy等数据处理工具对海量音乐数据进行深度挖掘和分析，实现了用户管理、音乐类型管理、音乐信息管理、歌曲信息管理、粉丝信息管理、歌单信息管理等核心业务功能模块。系统的亮点在于播放数预测管理功能，利用Spark强大的机器学习能力和HDFS分布式存储系统，能够对歌曲的未来播放量进行精准预测，为音乐平台的运营决策提供数据支撑。同时系统还集成了留言板管理、系统管理和个人中心等辅助功能，为用户提供完整的音乐体验服务，整个系统架构支持Python Django和Java SpringBoot双版本实现，满足不同开发需求和技术偏好。
音乐推荐系统演示视频
音乐推荐系统演示图片
音乐推荐系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, sum as spark_sum, avg, count, desc
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.recommendation import ALS
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("MusicRecommendationSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def music_recommendation_algorithm(user_id, top_n=10):
user_behavior_df = spark.sql("SELECT user_id, song_id, play_count, rating FROM user_behavior WHERE user_id = {}".format(user_id))
all_songs_df = spark.sql("SELECT song_id, song_name, artist, genre, popularity_score FROM songs")
user_history = user_behavior_df.collect()
if len(user_history) == 0:
popular_songs = spark.sql("SELECT song_id, song_name, popularity_score FROM songs ORDER BY popularity_score DESC LIMIT {}".format(top_n))
return [row.asDict() for row in popular_songs.collect()]
user_genres = spark.sql("SELECT s.genre, AVG(ub.rating) as avg_rating FROM user_behavior ub JOIN songs s ON ub.song_id = s.song_id WHERE ub.user_id = {} GROUP BY s.genre ORDER BY avg_rating DESC".format(user_id))
preferred_genre = user_genres.first()['genre'] if user_genres.count() > 0 else 'pop'
user_listened_songs = [row['song_id'] for row in user_history]
candidate_songs = all_songs_df.filter(~col('song_id').isin(user_listened_songs)).filter(col('genre') == preferred_genre)
similar_users_df = spark.sql("SELECT ub2.user_id, COUNT(*) as common_songs FROM user_behavior ub1 JOIN user_behavior ub2 ON ub1.song_id = ub2.song_id WHERE ub1.user_id = {} AND ub2.user_id != {} GROUP BY ub2.user_id HAVING common_songs >= 3 ORDER BY common_songs DESC LIMIT 20".format(user_id, user_id))
similar_user_ids = [row['user_id'] for row in similar_users_df.collect()]
if len(similar_user_ids) > 0:
collaborative_songs = spark.sql("SELECT ub.song_id, s.song_name, AVG(ub.rating) as avg_rating, COUNT(*) as listen_count FROM user_behavior ub JOIN songs s ON ub.song_id = s.song_id WHERE ub.user_id IN ({}) AND ub.song_id NOT IN ({}) GROUP BY ub.song_id, s.song_name ORDER BY avg_rating DESC, listen_count DESC LIMIT {}".format(','.join(map(str, similar_user_ids)), ','.join(map(str, user_listened_songs)) if user_listened_songs else '0', top_n))
return [row.asDict() for row in collaborative_songs.collect()]
content_based_songs = candidate_songs.orderBy(desc('popularity_score')).limit(top_n)
return [row.asDict() for row in content_based_songs.collect()]
def play_count_prediction(song_id, prediction_days=30):
historical_data = spark.sql("SELECT DATE(created_time) as play_date, COUNT(*) as daily_plays FROM play_records WHERE song_id = {} AND created_time >= DATE_SUB(CURRENT_DATE(), 90) GROUP BY DATE(created_time) ORDER BY play_date".format(song_id))
if historical_data.count() < 7:
return {"error": "insufficient_data", "predicted_plays": 0}
pandas_df = historical_data.toPandas()
pandas_df['play_date'] = pd.to_datetime(pandas_df['play_date'])
pandas_df['days_from_start'] = (pandas_df['play_date'] - pandas_df['play_date'].min()).dt.days
X = pandas_df[['days_from_start']].values
y = pandas_df['daily_plays'].values
vector_assembler = VectorAssembler(inputCols=['days_from_start'], outputCol='features')
spark_df = spark.createDataFrame(pandas_df[['days_from_start', 'daily_plays']])
feature_df = vector_assembler.transform(spark_df)
lr = LinearRegression(featuresCol='features', labelCol='daily_plays')
model = lr.fit(feature_df)
future_days = list(range(pandas_df['days_from_start'].max() + 1, pandas_df['days_from_start'].max() + prediction_days + 1))
future_df = spark.createDataFrame([(day,) for day in future_days], ['days_from_start'])
future_features = vector_assembler.transform(future_df)
predictions = model.transform(future_features)
predicted_values = [max(0, row['prediction']) for row in predictions.collect()]
total_predicted_plays = sum(predicted_values)
avg_daily_plays = total_predicted_plays / prediction_days
trend_analysis = "increasing" if len(predicted_values) > 1 and predicted_values[-1] > predicted_values[0] else "decreasing"
return {"song_id": song_id, "prediction_period_days": prediction_days, "total_predicted_plays": int(total_predicted_plays), "average_daily_plays": round(avg_daily_plays, 2), "trend": trend_analysis, "daily_predictions": [round(val, 2) for val in predicted_values]}
def user_behavior_analysis(user_id):
user_activity = spark.sql("SELECT COUNT(DISTINCT song_id) as unique_songs, COUNT(*) as total_plays, AVG(play_duration) as avg_duration FROM play_records WHERE user_id = {}".format(user_id))
activity_stats = user_activity.first()
genre_preference = spark.sql("SELECT s.genre, COUNT(*) as play_count, AVG(pr.play_duration) as avg_duration FROM play_records pr JOIN songs s ON pr.song_id = s.song_id WHERE pr.user_id = {} GROUP BY s.genre ORDER BY play_count DESC".format(user_id))
genre_stats = [{"genre": row['genre'], "play_count": row['play_count'], "avg_duration": round(row['avg_duration'], 2)} for row in genre_preference.collect()]
time_pattern = spark.sql("SELECT HOUR(created_time) as hour, COUNT(*) as play_count FROM play_records WHERE user_id = {} GROUP BY HOUR(created_time) ORDER BY hour".format(user_id))
hourly_pattern = {row['hour']: row['play_count'] for row in time_pattern.collect()}
peak_hours = sorted(hourly_pattern.items(), key=lambda x: x[1], reverse=True)[:3]
recent_activity = spark.sql("SELECT DATE(created_time) as activity_date, COUNT(*) as daily_plays FROM play_records WHERE user_id = {} AND created_time >= DATE_SUB(CURRENT_DATE(), 30) GROUP BY DATE(created_time) ORDER BY activity_date DESC".format(user_id))
daily_activity = [{"date": str(row['activity_date']), "plays": row['daily_plays']} for row in recent_activity.collect()]
favorite_artists = spark.sql("SELECT s.artist, COUNT(*) as play_count, COUNT(DISTINCT s.song_id) as unique_songs FROM play_records pr JOIN songs s ON pr.song_id = s.song_id WHERE pr.user_id = {} GROUP BY s.artist ORDER BY play_count DESC LIMIT 10".format(user_id))
artist_stats = [{"artist": row['artist'], "play_count": row['play_count'], "unique_songs": row['unique_songs']} for row in favorite_artists.collect()]
listening_diversity = len(genre_stats) / max(1, activity_stats['unique_songs']) if activity_stats['unique_songs'] > 0 else 0
engagement_score = (activity_stats['avg_duration'] / 180.0) * min(1.0, activity_stats['total_plays'] / 100.0) if activity_stats['avg_duration'] else 0
return {"user_id": user_id, "total_unique_songs": activity_stats['unique_songs'], "total_plays": activity_stats['total_plays'], "average_duration": round(activity_stats['avg_duration'], 2), "genre_preferences": genre_stats, "peak_listening_hours": [{"hour": hour, "play_count": count} for hour, count in peak_hours], "recent_daily_activity": daily_activity, "favorite_artists": artist_stats, "diversity_score": round(listening_diversity, 3), "engagement_score": round(engagement_score, 3)}
音乐推荐系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
