Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 240-245.doi: 10.11896/jsjkx.200700113

• Big Data & Data Science • Previous Articles     Next Articles

Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship

SHAO Chao, SONG Shu-mi   

  1. School of Computer and Information Engineering,Henan University of Economics and Law,Zhengzhou 450046,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:SHAO Chao,born in 1977,Ph.D,professor,M.S.supervisor.His main research interests include machine learning,data mining,etc.
  • Supported by:
    National Natural Science Foundation of China(61202285,61502146,61841702).

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Abstract: With the massive increase of information,the recommendation system has effectively alleviated the problems caused by the information explosion.Collaborative filtering,as one of the mainstream technologies of recommendation system,has been widely concerned.In the research of users' interest preference,the supervised data sets based on commodity labels are mainly studied,and the unsupervised data sets are ignored.At the same time,the influence of trusted users on users' interest is not considered in the process of calculating users' interest preference.To solve these problems,a collaborative filtering recommendation algorithm based on user preference under trust relationship is proposed in this paper.Firstly,the potential feature information of the items is obtained using the matrix factorization (MF) model,and then is clustered to obtain item type information.Secondly,the user trust relationship and users-item rating information are considered to construct the user preference matrix.Finally,the users are clustered based on the user preference matrix,and then the similarities between users in one cluster are calculated to implement recommendation.Experimental results on open datasets show that the algorithm can effectively improve the accuracy of recommendation results and the quality of recommendations.

Key words: Clustering, Collaborative filtering recommendation, Matrix factorization, Preference matrix, Similarity, Trust relationship

CLC Number: 

  • TP181
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