  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.
