An improved recommender model by joint learning of both similarity and latent feature space

Abstract

The matrix factorization recommender system based on manifold regularization, taking into account the similarity of local neighbors and manifold structure, can improve the quality of a recommendation system. However, the similarity between samples may not be accurate due to the sparsity of the data or the incompleteness of the tag information. Therefore, we propose a new model called SI-GMF (Similarity-learning-based Improved Graph Regularized matrix Factorization) by embedding the new similarity measure strategy in GMF (Graph Regularized matrix Factorization) framework, and induce three new matrix factorization algorithms (SI-GMF_1, SI-GMF_2, SI-GMF_3) based on three initial similarities by employing three different similarity measures. The solutions to the newly developed algorithms can be effectively obtained by SGD method. The experimental results show that the newly designed algorithms significantly improve the accuracy of a recommender system.