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Using Rich Social Media Information for Music Recommendation via Hypergraph Model

  • Shulong Tan
  • Jiajun Bu
  • Chun Chen
  • Xiaofei He

Abstract

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.

Keywords

Recommender System Mean Average Precision Recommendation Algorithm Query Vector Recommendation Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Zhejiang Key Laboratory of Service Robot, College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.State Key Laboratory of CAD&CG, College of Computer ScienceZhejiang UniversityHangzhouChina

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