Joint Modeling of Participant Influence and Latent Topics for Recommendation in Event-based Social Networks

Abstract

Event-based social networks (EBSNs) are becoming popular in recent years. Users can publish a planned event on an EBSN website, calling for other users to participate in the event. When a user is making a decision on whether to participate in an event in EBSNs, one aspect for consideration is existing participants defined as users who have agreed to join this event. Existing participants of the event may affect the decision of the user, to which we refer as participant influence. However, participant influence is not well studied by previous works. In this article, we propose an event recommendation model that considers participant influence, and exploits the influence of existing participants on the decisions of new participants based on Poisson factorization. The effect of participant influence is associated with the target event, the host group of the event, and the location of the event. Furthermore, our proposed model can extract latent event topics from event text descriptions, and characterize events, groups, and locations by distributions of event topics. Associations between latent event topics and participant influence are exploited for improving event recommendation. Besides making event recommendation, the proposed model is able to reveal the semantic properties of the participant influence between two users semantically. We have conducted extensive experiments on some datasets extracted from a real-world EBSN. Our proposed model achieves superior event recommendation performance over several state-of-the-art models. The results demonstrate that the consideration of participant influence can improve event recommendation.