Integrating collaborate and content-based filtering for personalized information recommendation

Authors:
Zhiyun Xin;Jizhong Zhao;Ming Gu;Jiaguang Sun
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China;School of Software, Tsinghua University, Beijing, China;School of Software, Tsinghua University, Beijing, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

To achieve high quality of push-based information service, in this paper, collaborative filtering and content-based adaptability approaches are surveyed for user-centered personalized information, then based on the above method, we proposed a mixed two-phased recommendation algorithm for high-quality information recommendation, upon which performance evaluations showed that the mixed algorithm is more efficient than pure content-based or collaborative filtering methods, for pure of either approaches is not so efficient for the lack of enough information need information. And moreover we found with large amount registered users, it is necessary and important for the system to serve users in a group mode, which involved merged retrieval issues.