Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (3): 168-173.doi: 10.11896/jsjkx.200700101

• Database & Big Data & Data Science • Previous Articles     Next Articles

Collaborative Filtering Recommendation Algorithm Based on Multi-context Information

HAO Zhi-feng1,2, LIAO Xiang-cai1, WEN Wen1, CAI Rui-chu1   

  1. 1 School of Computer Science and Technology,Guangdong University of Technology,Guangzhou 510006,China
    2 School of Mathematics and Big Data,Foshan University,Foshan,Guangdong 528000,China
  • Received:2020-07-15 Revised:2020-09-24 Online:2021-03-15 Published:2021-03-05
  • About author:HAO Zhi-feng,born in 1968,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include various aspects of algebra,machine learning,data mining and evolutionary alogrithms.
    WEN Wen,born in 1981, Ph.D,professor,is a member of China Computer Federation.Her main research interests include machine learning,graph embedding and sequential data analysis.
  • Supported by:
    National Natural Science Foundation of China(61876043,61976052) and Science and Technology Planning Project of Guangdong Province(2019A141401006).

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Abstract: With the development of e-commerce and the Internet,as well as the explosive growth of data information,collaborative filtering algorithm as a simple and efficient recommendation algorithm can effectively alleviate the problem of information explosion.However,the traditional collaborative filtering algorithm only uses a single rating to mine similar users,and the recommendation effect is not dominant.In order to improve the quality of personalized recommendations,how to make full use of the user (items) text,pictures,labels and other information to maximize the value of data is an urgent problem to be solved by the current recommendation system.Therefore,user-product interaction information is used as a bipartite graph,and different simila-rity networks are constructed according to the characteristics of different contexts.The design objective function is combined with matrix decomposition under the constraints of various information networks and user or item embedding can be gotten.Extensive experiments are conducted on multiple data sets,and the results show that the collaborative filtering algorithm by fusion of multiple types of information can effectively improve the accuracy of recommendations and alleviate the problem of data sparsity.

Key words: Collaborative filtering, Matrix decomposition, Multi-context information, Recommendation system

CLC Number: 

  • TP181
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