Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems

Matrix factorization (MF) is an increasingly important approach in the field of missing value prediction because recommender systems are rapidly becoming ubiquitous. MF-based collaborative filtering (CF) seeks to improve recommender performance by combining user-item matrix with MF. However, most MF-based approaches available at present could not obtain high prediction accuracy because of the sparse availability of user-item matrices in CF models. The present paper proposes a framework that involves two efficient MF, dynamic single-element-based CF-integrating manifold regularization (DSMMF) and dynamic single-element-based Tikhonov graph regularization non-negative MF (DSTNMF). The aim of this framework is to better use the intrinsic structure of user-item rating matrix and user/item content information, overcome the dimensionality curse and ill-posed problem of weighted graph NMF, and evade the frequent manipulations of indicator matrices that lack practicability. We validate the effectiveness of our proposed algorithms with respect to recommender performance by four indices on three datasets. We demonstrate that our proposed approaches lead to considerable improvement compared with several other state-of-the-art approaches.