Modeling Users' Exposure with Social Knowledge Influence and Consumption Influence for Recommendation.

Users' consumption behaviors are affected by both their personal preference and their exposure to items (i.e. whether a user knows the items).Most of the recent works in social recommendation assume that people share similar preference with their socially connected friends. However, this assumption may not hold due to the diversity of social relations, and modeling social influence on users' preference may not be suitable for implicit feedback data (i.e. whether a user has consumed certain items). Since users often share item information with their social relations, it will be less restrictive to model social influence on users' exposure to items. We notice that a user's exposure is affected by the exposure of the other users in his social communities and by the consumption of his connected friends. In this paper, we propose a novel social exposure-based recommendation model SoEXBMF by integrating two kinds of social influence on users' exposure, i.e. social knowledge influence and social consumption influence, into basic EXMF model for better recommendation performance. Furthermore, SoEXBMF uses Bernoulli distribution instead of Gaussian distribution in EXMF to better model the binary implicit feedback data. A variational inference method has been developed for the proposed SoEXBMF model to infer the posterior and make the recommendations. Extensive experiments on three real-world datasets demonstrate the superiority of our method over existing methods in various evaluation metrics.