Matrix Factorization Method for Decentralized Recommender Systems

2016/04/28 by Wenjie Zheng, Zheng, Wenjie
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Distributed #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Parallel #Recommender Systems and Techniques #and Cluster Computing (cs.DC) #cs.DC #cs.IR

paper · pdf · doi:10.48550/arxiv.1604.08420

arxiv created 2016/04/28 · openalex publication_date 2016/04/28 · arxiv updated 2016/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Decentralized recommender system does not rely on the central service provider, and the users can keep the ownership of their ratings. This article brings the theoretically well-studied matrix factorization method into the decentralized recommender system, where the formerly prevalent algorithms are heuristic and hence lack of theoretical guarantee. Our preliminary simulation results show that this method is promising.

Citations

• Matrix Factorization Techniques for Recommender Systems
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

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