Fuzzy Recommendation Algorithm for Asymmetric Heterogeneous Information Network

WANG Yonggui, MEI Xuanwei

1. College of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
• Online:2020-12-01 Published:2020-11-30

非对称异构信息网络的模糊推荐算法

1. 辽宁工程技术大学 软件学院，辽宁 葫芦岛 125105

Abstract:

The traditional collaborative filtering recommendation algorithm has a common problem of data sparsity; heterogeneous information network models applied in the field of recommendation algorithm usually identify the similarity relationship of objects symmetrically, which has limitations in the actual problem processing. To solve these problems, a fuzzy recommendation algorithm for asymmetric heterogeneous information networks is proposed. Firstly, the algorithm uses the advantage of fuzzy set theory in dealing with the degree of user preference. Then, according to the rich semantic information of meta path in heterogeneous information network, the user association from different angles is obtained. Then, the asymmetric coefficient of object relationship is introduced in the similarity calculation, and the calculation results of different feature element paths are weighted. In order to improve the accuracy of similarity relationship between users, the score prediction is realized by matrix decomposition method. Experimental results show that the algorithm effectively solves the problem of data sparsity and improves the recommendation accuracy.