Alleviating the Sparsity Problem in Recommender Systems by Exploring Underlying User Communities

Abstract. Collaborative Filtering, one of the main Recommender Sys-tems ’ approach, has been successfully employed to identify users and items that can be characterized as similar in large datasets. However, its application is limited due to the sparsity problem, which refers to a situation where information to infer similar users and predict items is missing. In this work, we address this by (i) detecting underlying user communities that aggregate similar tastes and (ii) predicting new rela-tions within communities. As a consequence, we alleviate some of the major consequences of this problem. As shown by our experiments, our method is promising. When compared to a user-based Collaborative Fil-tering method, it provided gains of 20.2 % in terms of sparsity decay, for instance, while improving RMSE values in only 2.8%.