  This paper is concerned with how to make efficient use of social information
to improve recommendations. Most existing social recommender systems assume
people share similar preferences with their social friends. Which, however, may
not hold true due to various motivations of making online friends and dynamics
of online social networks. Inspired by recent causal process based
recommendations that first model user exposures towards items and then use
these exposures to guide rating prediction, we utilize social information to
capture user exposures rather than user preferences. We assume that people get
information of products from their online friends and they do not have to share
similar preferences, which is less restrictive and seems closer to reality.
Under this new assumption, in this paper, we present a novel recommendation
approach (named SERec) to integrate social exposure into collaborative
filtering. We propose two methods to implement SERec, namely social
regularization and social boosting, each with different ways to construct
social exposures. Experiments on four real-world datasets demonstrate that our
methods outperform the state-of-the-art methods on top-N recommendations.
Further study compares the robustness and scalability of the two proposed
methods.

Information Retrieval;Social and Information Networks