Low-rank and sparse matrix factorization with prior relations for recommender systems

Abstract

The explosive growth of data has caused users to spend considerable time and effort finding the items they need. Various recommender systems have been created to provide convenience for users. This paper proposes a low-rank and sparse matrix factorization with prior relations (LSMF-PR) recommendation model, which predicts users’ ratings for items through a sum of the learned low-rank matrix and sparse matrix. Thus, unlike traditional matrix factorization approaches, our method can alleviate the error propagation produced by intermediate outputs. The LSMF-PR integrates user relationships and item relationships as prior information. User relationships in different recommendation scenarios are extracted by the corresponding social relations of the users, and item relationships are obtained from the similarity of the item content. Therefore, the sparsity and cold start problems can be effectively reduced with prior information. Furthermore, our model has better interpretability since it reveals the low-rank and sparse features of the ratings. Experiments are conducted on four real-world datasets to validate the performance of our proposed method.