Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 199-203.

• Data Science • Previous Articles     Next Articles

User Collaborative Filtering Recommendation Algorithm Based on All Weighted Matrix Factorization

DENG Xiu-qin1, LIU Tai-heng1, LIU Fu-chun2, LONG Yong-hong1   

  1. (School of Applied Mathematics,Guangdong University of Technology,Guangzhou 510006,China)1;
    (School of Computers,Guangdong University of Technology,Guangzhou 510006,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: Aiming at the problem that traditional user collaborative filtering recommendation algorithm equates users’ preferences for an item,a user collaborative filtering model based on all weighted matrix decomposition was proposed.Firstly,the model designs frequency sensing weights for observations,and non-uniformly designs user-oriented weights for unobserved values.Then,the weights of the observed and unobserved values are combined,and the similarity between user reputation and user relationship is determined according to the score,and the user collaborative filtering model of the fused fully weighted matrix decomposition is constructed.In order to verify the performance of the proposed recommendation algorithm,experiments were carried out on three real data sets:Douban,Epinions and Last.fm.The experimental results demonstrate that the proposed AWMF_UCFR algorithm achieves significant improvements on recommendation accuracy than MF algorithm,WRMF-UO algorithm and SoRS algorithm.

Key words: All-weighted matrix factorization, Collaborative filtering algorithm, Recommendation algorithm, Social network

CLC Number: 

  • TP391
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