A fusion collaborative filtering method for sparse data in recommender systems

Abstract

Collaborative filtering is a fundamental technique in recommender systems, for which memory-based and matrix-factorization-based collaborative filtering are the two types of widely used methods. However, the performance of these two types of methods is limited in the case of sparse data, particularly with extremely sparse data. To improve the effectiveness of the methods in a sparse scenario, this paper proposes a multi-factor similarity measure that captures linear and nonlinear correlations between users resulting from extreme behavior. Subsequently, a fusion method that simultaneously considers the multi-factor similarity and the global rating information in a probability matrix factorization framework is proposed. In our framework, users’ local relations are integrated into the global ratings optimization process, so that prediction accuracy and robustness are improved in sparse data, particularly in extremely sparse data. To verify the performance of the proposed methods, we conduct experiments on four public datasets. The experimental results show that the fusion method is superior to the typical matrix factorization models used in collaborative filtering and significantly improves both the prediction results and robustness in sparse data.