Collaborative Filtering Recommendation Algorithm Incorporating Social Network Information
GUO Lanjie, LIANG Jiye, ZHAO Xingwang
School of Computer and Information Technology, Shanxi University, Taiyuan 030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract To solve the problems of high data sparsity and limited recommendation precision of collaborative filtering recommendation algorithms, a collaborative filtering algorithm incorporating social network information is proposed under the framework of item-based collaborative filtering recommendation. In item similarity calculation period and user rating prediction period, social network information is utilized to fill missing values in rating matrix selectively and thus the existing rating information is utilized as much as possible. Finally, experiment is conducted on Epinions dataset. Results show that the proposed algorithm alleviates the data sparsity problem and outperforms other collaborative filtering algorithms on rating error and precision.
Fund:Supported by National Natural Science Foundation of China (No.61573229,61432011,U1435212), Construction Project of Science and Technology Basic Condition Platform of Shanxi Province (No.2012091002-0101), Key Technology R&D Program of Shanxi Province (No.20110321027-01)
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2016, Vol. 29 
