Exploiting Implicit Item Relationships for Recommender Systems

Zhu Sun, Guibing Guo, Jie Zhang

Abstract: Collaborative filtering inherently suffers from the data sparsity and cold start problems. Social networks have been shown useful to help alleviate these issues. However, social connections may not be available in many real systems, whereas implicit item relationships are lack of study. In this paper, we propose a novel matrix factorization model by taking into account implicit item relationships. Specifically, we employ an adapted association rule technique to reveal implicit item relationships in terms of item-to-item and group-to-item associations, which are then used to regularize the generation of low-rank user- and item-feature matrices. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach against other counterparts.