Regularized Matrix Factorization with cognition degree for collaborative filtering

摘要：

　　Collaborative filtering is widely used technique in Recommender systems(RS)that are designed to deal with information overload problem.In particular,recently proposed methods based on Regularized Matrix Factorization(RMF)have shown promising results.However,these approaches focus on the user-item rating matrix,but ignore the significant influence of users preferences on items.In this paper,borrowed the idea of cognition degree,we propose a novel cognition degree-based RMF collaborative filtering model named CogRMF that model the interactions between users and items with users cognition degrees.In addition,Experiments on the real dataset Movielens 1M are implemented.Empirical outcomes show that the proposed model obtains significantly better results than other benchmark methods,such as user-based collaborative filtering(UCF),item-based collaborative filtering(ICF),cognition degree-based collaborative filtering(CDCF)and Regularized Matrix Factorization(RMF).

关键词： Collaborative Filtering Regularized Matrix Factorization Cognition Degree

作者: JieMin Chen Yong Tang JianGuo Li Jing Xiao WenLi Jiang

作者单位: School of Computer Science,South China Normal University,Guangzhou,China

会议类型: 国际会议

会议名称: The 9th International Conference on Pervasive Computing and Application(ICPCA 2014)(第九届全国普适计算学术会议、第九届全国人机交互联合学术会议)

会议地点: 南昌

会议语种:英文

页码: 1-12

在线出版日期: 2013-09-26（万方平台首次上网日期，不代表论文的发表时间）