Improving recommendation based on features' co-occurrence effects in collaborative tagging systems

Authors:
Hao Han;Yi Cai;Yifeng Shao;Qing Li
Affiliations:
School of Software Engineering, South China University of Technology, Guangzhou, China;School of Software Engineering, South China University of Technology, Guangzhou, China;School of Software Engineering, South China University of Technology, Guangzhou, China;Department of Computer Science, City University of Hongkong, Hongkong, China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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Abstract

Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment.