中文摘要：

针对基于音乐作品信息的音乐作品个性化推荐及协同过滤方法的不足,通过分析音乐作品需求者的音乐试听数据及下载数据,并结合LDA（latent Dirichlet allocation）主题挖掘模型,提出一种基于LDA-MURE模型的推荐算法.实验结果表明,与基于音乐作品需求者的协同过滤算法和基于音乐属性项目的协同过滤算法相比,LDA-MURE算法可更高效地向音乐作品需求者推荐感兴趣的音乐作品.

英文摘要：

Aiming at the lack of personalized music recommendation and collaborative filtering method based on music information,through the analysis of the user＇s listening to music data and download data,combined with LDA（latent Dirichlet allocation）theme mining model, we proposed a recommendation algorithm based on the LDA-MURE model.Experimental results show that,compared with collaborative filtering algorithm based on user of music works and collaborative filtering algorithm based on music attribute item,the LDA-MURE algorithm can be more effective to music users recommend music works of interest.