Abstract:
The popularity of the Internet and electronic music resources makes it easier for people to obtain music resources.However,as the music library becomes larger and more abundant,it is difficult to find favorite music accurately and timely.Therefore,on music sites,a suitable music recommendation algorithm is particularly needed for users.According to the deficiencies of music recommendation based on audio information and the collaborative filtering method,this paper analyzes the user’s music listening data and download data,combined with the Latent Dirichlet Allocation(LDA) theme mining model,proposes a music recommendation algorithm.Experimental results show that compared with the collaborative filtering algorithm based on user and the collaborative filtering algorithm based on item,MR_LDA algorithm can be more efficiently recommend interested music to users.
Key words:
collaborative filtering,
music recommendation,
theme mining,
Latent Dirichlet Allocation(LDA) model,
Gibbs sampling,
Music Recommendation based on LDA model(MR_LDA)
摘要： 互联网的普及以及音乐资源的电子化使得人们可以更方便地获得音乐资源。但随着音乐库变得越来越大、资源越来越丰富,人们已经很难准确及时地找到自己喜欢的音乐。因此,对于音乐网站而言,需要一个合适的音乐推荐算法向用户推荐音乐。根据已有的基于音频信息的音乐推荐以及协同过滤方法,分析用户的音乐试听数据以及下载数据,并结合Latent Dirichlet分配(LDA)主题挖掘模型,提出一种音乐推荐算法。实验结果表明,与基于用户的协同过滤算法以及基于项目的协同过滤算法相比,该算法可以更加高效地向用户推荐感兴趣的音乐。
关键词:
协同过滤,
音乐推荐,
主题挖掘,
Latent Dirichlet分配模型,
吉布斯抽样,
基于LDA模型的音乐推荐
CLC Number: