Decentralized Recommender Systems

TL;DR

This paper introduces a decentralized recommender system that distributes data storage and computation across users, enhancing privacy, scalability, and robustness while maintaining competitive recommendation performance.

Contribution

It formulates collaborative filtering as a decentralized matrix completion problem, enabling fully distributed data and computation without centralized fusion.

Findings

Decentralized algorithm achieves comparable performance to state-of-the-art methods.

The system enhances user privacy by limiting data exchange.

Improves scalability and robustness over traditional centralized recommenders.

Abstract

This paper proposes a decentralized recommender system by formulating the popular collaborative filleting (CF) model into a decentralized matrix completion form over a set of users. In such a way, data storages and computations are fully distributed. Each user could exchange limited information with its local neighborhood, and thus it avoids the centralized fusion. Advantages of the proposed system include a protection on user privacy, as well as better scalability and robustness. We compare our proposed algorithm with several state-of-the-art algorithms on the FlickerUserFavor dataset, and demonstrate that the decentralized algorithm can gain a competitive performance to others.