Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
This work provides a more realistic and less restrictive method for social recommendation, benefiting users by offering more accurate item suggestions based on social exposure rather than potentially inaccurate preference assumptions.
This paper addresses the challenge of integrating social information into recommender systems by modeling user exposures to items through their social network, rather than assuming shared preferences. The proposed approach, SERec, implemented via social regularization and social boosting, significantly outperforms state-of-the-art methods in top-N recommendations across four real-world datasets.
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.