CCCF: Improving collaborative filtering via scalable user-item co-clustering

Abstract

Collaborative Filtering (CF) is the most popular method for recommender systems. The principal idea of CF is that users might be interested in items that are favorited by similar users, and most of the existing CF methods measure users' preferences by their behaviors over all the items. However, users might have different interests over different topics, thus might share similar preferences with different groups of users over different sets of items. In this paper, we propose a novel and scalable method CCCF which improves the performance of CF methods via user-item co-clustering. CCCF first clusters users and items into several subgroups, where each subgroup includes a set of like-minded users and a set of items in which these users share their interests. Then, traditional CF methods can be easily applied to each subgroup, and the recommendation results from all the subgroups can be easily aggregated. Compared with previous works, CCCF has several advantages including scalability, flexibility, interpretability and extensibility. Experimental results on four real world data sets demonstrate that the proposed method significantly improves the performance of several state-of-the-art recommendation algorithms.