Coupled Bayesian Matrix Factorization in Recommender Systems
Xueci Zhao, Chengzhang Zhu, Lizhi Cheng · 2017
In recommender systems, users' preference and items' attraction are heavily determined by users' and items' attributes information. The aim of this study was to incorporate these attributes information into a matrix factorization model. In this paper, users' and items' attributes were used to calculate similarity, and then a nonparametric Bayesian method was applied to cluster users and cluster items, based on the similarity. These clustering results were used to mine coupling relationships and improve the accuracy of recommendation. Experimental results on Movielens 1M demonstrate the superiority of our proposed model over existing methods.