基于用户-标签-项目语义挖掘的个性化音乐推荐

个性化推荐系统面临的难题是推荐的准确性、多样性以及新颖性，同时其数据集存在稀疏、信息缺失(如用户描述、项目属性以及明确的评分)等问题.协同标注中的标签包含丰富的个性化描述信息以及项目内容信息，因此可以用来帮助提供更好的推荐.算法以二部图节点结构相似与重启型随机游走为基础，分析音乐社交网络Last.fm中用户、项目、标签两两之间的联系,首先构建音乐间及标签间的相邻关系，初步得到音乐推荐列表和间接关联音乐集合，然后按所提算法融合结果，重新排序，得到最终推荐列表，从而实现个性化音乐推荐算法.实验表明，在该数据集上,所提方法能够满足用户对音乐的个性化需求.

Mining Latent Semantic on User-Tag-Item for Personalized Music Recommendation

Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks of accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain abundant information about personalized preferences and item contents, and are therefore potential to help providing better recommendations. In this paper, we analyze the information on the famous music social network, Last.fm. Bipartite graph is established between users, items and tags while random walk with restart is used to analyze the relationship between the nodes discussed before and get the neighboring relations between songs or tags. After that, musicrecommended list and indirect related music collection, thus, can be obtained. At last, personalized music recommendation algorithm can be implemented by fusing and reranking the recommended list using the algorithm proposed in this paper. Experiments show that, in the same corpus, the music recommendation algorithmin this paper performs better than the ordinary method such as collaborative filtering and bipartite based algorithm. Our method built on Last.fm, therefore, satisfies the personalized requirement for users to music. Furthermore, with the development of Web2.0, our method will show its advantage as the amount of tags become more and more enormous.