On top-N recommendation using implicit user preference propagation over social networks

Social recommender systems exploit the historic user data as well as user relations in the social networks to make recommendations. However, users are increasingly concerned with their online privacy, and hence, they are not willing to reveal their personal data to the general public. In this paper, we propose a social recommendation algorithm for top-N recommendation using only implicit user preference data. In particular, we model users' consumption behavior in the social network with Bayesian networks, using which we can infer the probabilities for items to be selected by each user. We develop an Expectation Propagation (EP) message-passing algorithm to perform approximate inference efficiently in the constructed Bayesian network. The original proposed algorithm is a central scheme, in which the user data are collected and processed by a central authority. However, it can be easily adapted for a distributed implementation, where users only exchange messages with their directly connected friends in the social network. This helps further protect user privacy, as users do not release any data to the public. We evaluate the proposed algorithm on the Epinions dataset, and compare it with other existing social recommendation algorithms. The results show its superior top-N recommendation performance in terms of recall.