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.
@article{arxiv.1503.01647,
title = {Decentralized Recommender Systems},
author = {Zhangyang Wang and Xianming Liu and Shiyu Chang and Jiayu Zhou and Guo-Jun Qi and Thomas S. Huang},
journal= {arXiv preprint arXiv:1503.01647},
year = {2015}
}