A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method

• Published in: Knowledge-based systems 2019-02, Vol.166, p.92-107
• Main Authors: Parvin, Hashem, Moradi, Parham, Esmaeili, Shahrokh, Qader, Nooruldeen Nasih
• Format: Article
• Language: English
• Subjects: Algorithms; Alternating direction method; Cold starts; Collaborative filtering; Computer memory; Factorization; Matrix factorization; Recommender systems; Regularization; Regularization methods; Social trust
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2018.12.016

Matrix Factorization (MF) has been proven to be an effective approach for the generation of a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of the user–item matrix in collaborative filtering models. Moreover, they suffer from scalability issues when applied to large-scale real-world tasks. To tackle these issues, a social regularization method, called TrustANLF, is proposed, which incorporates users’ social trust information in a nonnegative matrix factorization framework. The proposed method integrates trust statements as an additional information source along with rating values into the recommendation model to deal with the data sparsity and cold-start issues. Moreover, the alternating direction optimization method is used for solving the trust-based nonnegative MF model in order to improve convergence speed as well as reduce computational and memory costs. To evaluate the effectiveness of the proposed method, several experiments are performed on three real-world datasets. The obtained results demonstrate the significant improvements of the proposed method over several state-of-the-art methods. •A social regularization recommender system method called TrustANLF is proposed.•TrustANLF incorporates the social trust information of users in the NMF framework.•Trust statements are used to deal with the data sparsity and cold-start issues.•Alternating direction optimization method is used to improve the convergence speed.•The results of experiments reveal the effectiveness of the proposed method.