Year: 2015
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Wang, Zhangyang, Liu, Xianming, Chang, Shiyu, Zhou, Jiayu, Qi, Guo-Jun, Huang, Thomas S.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1527292425