Distributed Collaborative Filtering with Cascaded Belief Propagation

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. Previously, we proposed a semi-distributed Belief Propagation (BP) approach to privacy-preserving item-based CF recommender systems. 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.

However, the semi-distributed BP architecture requires that all users be active and participate in BP message propagation at the same time. Yet, in practical scenarios, it is difficult to meet this stringent requirement due to various reasons, e.g., some users may not be active temporally. In this project, 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. Moreover, we analyze the privacy of the semi-distributed BP from a information-theoretic perspective.