Collaborative filtering based on information-theoretic co-clustering

Abstract

Collaborative filtering is one of the most popular recommendation techniques, which provides personalised recommendations based on users’ tastes. In spite of its huge success, it suffers from a range of problems, the most fundamental being that of data sparsity. Sparsity in ratings makes the formation of inaccurate neighbourhood, thereby resulting in poor recommendations. To address this issue, in this article, we propose a novel collaborative filtering approach based on information-theoretic co-clustering. The proposed approach computes two types of similarities: cluster preference and rating, and combines them. Based on the combined similarity, the user-based and item-based approaches are adopted, respectively, to obtain individual predictions for an unknown target rating. Finally, the proposed approach fuses these resultant predictions. Experimental results show that the proposed approach is superior to existing alternatives.