Using Rich Social Media Information for Music Recommendation via Hypergraph Model

被引:64
作者
Tan, Shulong [1 ]
Bu, Jiajun [1 ]
Chen, Chun [1 ]
Xu, Bin [1 ]
Wang, Can [1 ]
He, Xiaofei [2 ]
机构
[1] Zhejiang Univ, Zhejiang Key Lab Serv Robot, Coll Comp Sci, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ, State Key Lab CAD&CG, Coll Comp Sci, Hangzhou 310027, Peoples R China
基金
中国国家自然科学基金;
关键词
Algorithms; Experimentation; Recommender system; music recommendation; hypergraph; social media information;
D O I
10.1145/2037676.2037679
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last. fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.
引用
收藏
页数:22
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