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Social Tagging Recommendation Based on Content Analysis and Tag Expansion

  

  1. School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2015-11-30 Online:2016-05-24 Published:2016-05-25

Abstract:

With the rapid development of social tagging system such as Douban and CiteULike, the social tagging recommendation technology has become a hot research topic
recently. However, compared to traditional recommendation system, social tagging system has a new evaluation criterion and tag, which leads to the problems of low tag quality,
data sparsity, cold start and unified model applicability and so on. Hence, the traditional recommendation method can’t achieve satisfactory results. This paper proposed TECA
(Tag Expansion and Content Analysis), a method based on tag expansion and content analysis, and used TECA to implement tag and user recommendation. TECA can use classification
to refine the resource model, use semantic topic to recommend tags and use tag expansion to alleviate data sparsity. The experiment on CiteULike’s dataset shows TECA achieves
better tag and user recommendation performance than traditional collaborative filtering recommendation method.

Key words: social tagging system, tag recommendation, user recommendation, tag expansion, content analysis

CLC Number: