A Comprehensive Social Matrix Factorization with Social Regularization for Recommendation Based on Implicit Similarity by Fusing Trust Relationships and Social Tags Rui Chen (
[email protected] ) Zhengzhou University of Light Industry Jianwei Zhang Zhengzhou University of Light Industry Zhifeng Zhang Zhengzhou University of Light Industry Yan-Shuo Chang Xi'an University of Finance and Economics Jingli Gao Pingdingshan University Pu Li Zhengzhou University Hui Liang Zhengzhou University of Light Industry Research Article Keywords: Recommender Systems, Collaborative ltering, Matrix factorization, Social relationships, Social networks Posted Date: August 24th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-490657/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Soft Computing (2019) 77:21823 http://doi.org/10.1007/s11042-6392-8 A Comprehensive Social Matrix Factorization with Social Regularization for Recommendation Based on Implicit Similarity by Fusing Trust Relationships and Social Tags Rui Chen1,2, Jian-wei Zhang1,2,*, Zhifeng Zhang1,2, Yan-Shuo Chang3,*, Jingli Gao4, Pu Li1,2, and Hui Liang1,2 Received: 8 Jun 2020 / Revised: ×× ×× 2020 / Accepted: ×× ×× 2020/ Published online: 12 September 2020 Abstract Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, since the social relationships between users are extremely sparse and complex, and it is difficult to obtain accurately user preference model, thus the performance of the recommendation system is affected by the existing social recommendation methods.