Matrix Factorization Method for Decentralized Recommender Systems

TL;DR

This paper introduces a matrix factorization approach for decentralized recommender systems, enabling users to retain data ownership while providing a theoretically grounded alternative to heuristic algorithms.

Contribution

It adapts matrix factorization to decentralized settings, offering a theoretically supported method for recommender systems without central authority.

Findings

Preliminary simulations show promising results.

The method provides a theoretical guarantee absent in previous heuristics.

Potential for improved privacy and ownership in recommender systems.

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.