Modeling Social Behavior in Collaborative Filtering

Nowadays, many online services use recommendation systems to provide personalized item recommendations to users. Collaborative filtering is the major paradigm in recommendation systems. Based on user-item interaction data, collaborative filtering recommends items to a user based on other similar users. The problem of interest disentanglement in recommendation now has attracted the attention of many researchers. Several works have proposed methods to disentangle conformity from user private interest, by assuming that conformity is correlated to item popularity. However, such modeling is simplistic and overlooks many possibilities between user public and private interest, and the item popularity. For example, a user can privately like a popular movie or buy a niche music album due to the stimulation of the social environment. In this paper, we propose a more comprehensive social behavior model that describes fine-grained relationships between user interest and item popularity. Our model does not use explicit user relationship data. Instead, we extract social behavior patterns directly from user-item interaction data. We also make our model into a recommendation framework called Disentangled Social Consumer Preference (DSCP), which can be integrated into existing recommendation models such as BPRMF. Our extensive experiments with four datasets from different services show that our model can outperform state-of-the-art baseline models. We achieve better recommendation accuracy in both the usual random test and the intervened test that shows debiasing effect.