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arXiv · 1503.01647

Decentralized Recommender Systems

Zhangyang Wang·Xianming Liu·Shiyu Chang·Jiayu Zhou·Guo-Jun Qi·Thomas S. Huang
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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.

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Zhangyang Wang, Xianming Liu, Shiyu Chang, Jiayu Zhou, Guo-Jun Qi, Thomas S. Huang. 2015-03-05. Decentralized Recommender Systems. https://arxiv.org/abs/1503.01647

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