关键词: 模糊集, 异构信息网络, 元路径, 非对称系数

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.

Key words: fuzzy set, heterogeneous information network, meta-path, asymmetry coefficient