关键词: 异质信息网络, 元路径, 相似度, 矩阵分解

Abstract:

In recent years, as a new research direction, heterogeneous information networks have attracted a lot of attention in recommendation system. Most similarity measures based on heterogeneous information networks follow that the relationship between users is symmetrical, but in real life, due to the number of items rated by different users are various, it sometimes makes the relationship asymmetrical. In order to measure the relationship more accurately between users, first of all, the asymmetric coefficient is employed to characterize the asymmetry of similarity based on the mean square deviation formula. Then according to the characteristics of meta-paths, different weights of meta-paths are given, and the similarity results are weighted to improve accuracy. Finally, the recommendation for rating prediction based on heterogeneous information network is implemented combined with similarity information and rating information in matrix factorization model. The experimental results on dataset demonstrate that the proposed algorithm is superior to the traditional algorithms in the evaluation of mean absolute error and root mean square error.

Key words: heterogeneous information network, meta-path, similarity, matrix factorization