Matrix Factorization Method for Decentralized Recommender Systems
This work addresses the need for decentralized recommender systems that allow users to retain ownership of their ratings, though it appears incremental by adapting an existing method to a new context.
The paper tackles the problem of decentralized recommender systems by applying matrix factorization, a theoretically well-studied method, to replace heuristic algorithms lacking theoretical guarantees, with preliminary simulation results indicating promise.
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.