A Belief Propagation Approach to Privacy-Preserving Item-Based Collaborative Filtering

Abstract

Collaborative filtering (CF) is the most popular recommendation algorithm, which exploits the collected historic user ratings to predict unknown ratings. However, traditional recommender systems run at the central servers, and thus users have to disclose their personal rating data to other parties. This raises the privacy issue, as user ratings can be used to reveal sensitive personal information. In this paper, we propose a semi-distributed belief propagation (BP) approach to privacy-preserving item-based CF recommender systems. 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. We further propose a cascaded BP scheme to address the practical issue that only a subset of users participate in BP during one time slot. We analyze the privacy of the semi-distributed BP from a information-theoretic perspective. We also propose a method that reduces the computational complexity at the user side. Through experiments on the MovieLens dataset, we show that the proposed algorithm achieves superior accuracy.