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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Speaker: Jun Zou
Abstract:
Recommender systems are increasingly employed by e-commerce websites to suggest items to users that meet their preferences. Collaborative Filtering (CF), as the most popular recommendation algorithm, exploits the collected historic user ratings to predict ratings on unseen items for users. However, traditional recommender systems are run by the commercial websites, and thus users have to disclose their personal rating data to the websites in order to receive recommendations. This raises the privacy issue, as user ratings can be used to reveal sensitive personal information. In this paper, we propose a privacy-preserving item-based CF recommender system using semi-distributed Belief Propagation (BP), where rating data are stored at the user side. Firstly, we formulate the item similarity computation as a probabilistic inference problem on the factor graph, which can be efficiently solved by applying the BP algorithm. To avoid disclosing user ratings to the server or other user peers, we then introduce a semi-distributed architecture for the BP algorithm, where only probabilistic messages on item similarity are exchanged between the server and users. Finally, an active user locally generates rating predictions by averaging his own ratings on items weighted by their similarities to unseen items. As such, the proposed recommender system preserves user privacy without relying on any privacy techniques, e.g., obfuscation and cryptography. Further, there is no compromise in recommendation performance compared to the centralized counterpart of the proposed algorithm. Through experiments on the MovieLens dataset, we show that the proposed algorithm achieves superior accuracy.
Bio:
Jun Zou is working towards his Ph.D. degree in electrical and computer engineering at Georgia Institute of Technology, under the supervision of Prof. Faramarz Fekri. The focus of his current research is on recommender and reputation systems. Prior to this, he was a dual-masters student at both Georgia Institute of Technology and Shanghai Jiao Tong University, from which he obtained an M.S. degree in electrical and computer engineering and an M.S. degree in communication and information systems, respectively, both in 2012. Zou earned his B.E. degree from the Department of Electronic Engineering at Shanghai Jiao Tong University, Shanghai, China, in 2009.
