Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (9): 65-70.doi: 10.3969/j.issn.1000-565X.2013.09.011

• Computer Science & Technology • Previous Articles     Next Articles

Tag Group Effect- Based Recommendation Algorithm for Collaborative Tagging Systems

Cai Yi Liu Yu Zhang Guang- yi Chen Jun- ting Min Hua- qing   

  1. School of Software Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China
  • Received:2013-03-06 Online:2013-09-25 Published:2013-08-01
  • Contact: 蔡毅(1980-)，男，博士，副教授，主要从事数据挖掘、信息检索研究． E-mail:ycai@scut.edu.cn
  • About author:蔡毅(1980-)，男，博士，副教授，主要从事数据挖掘、信息检索研究．
  • Supported by:

    国家自然科学基金资助项目(61300137);广东省自然科学基金资助项目(S2011040002222);广东省优秀青年创新人才培育项目(LYM11019);华南理工大学中央高校基本科研业务费专项资金资助项目(2012ZM0077);国家大学生创业创新训练计划项目(201210561106， 201210561108)

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Abstract:

In the existing user modeling methods for collaborative tagging systems,a user is regarded as a tag- vector and it is assumed to be interested in every tag in the tag- vector.Moreover,only the matching degree of a tag with another tag is calculated,while the effects of tags as a whole on the user’ s preference are ignored.In order to solve these problems,this paper proposes a recommendation algorithm based on the tag- group effect,namely,TGER.This algorithm utilizes the user ratings on resources to select the tag- groups which have significant effects on the user’ s preference,and adopts the high- dimension tag- group first matching method to calculate the user- resource relevance.Experimental results on the MovieLens data set show that TGER can significantly improve the recommendation qua-lity.

Key words: collaborative tagging system, tag- group effect, user modeling, recommendation algorithm