Collaborative Filtering Based on Rough Set-Based Co-clustering Induced by Multinomial Mixture Models Considering Uncertainty

Abstract

In clustering-based collaborative filtering (CF), clusters of users with similar preference patterns are extracted, and items with high preferences within the cluster are recommended. Since data in CF tasks contain uncertainties arising from human sensibilities, represented as co-occurrence relationships between users and items, approaches such as rough clustering and co-clustering can be effective. Thus, rough co-clustering induced by multinomial mixture models (RCCMM) and its application to CF (RCCMM-CF) have been proposed. However, RCCMM has a problem in that it does not consider the granularity, an important viewpoint in rough set theory. In this study, we propose a CF approach based on rough set-based co-clustering induced by multinomial mixture models (RSCCMM) considering granularity. Furthermore, we verify the recommendation performance of the proposed method through numerical experiments using real-world datasets.