Event-based Social Networks (EBSN) are popular for organizing offline social events nowadays. In this paper, we develop a new model for event recommendation on EBSNs, which exploits the influence of existing participants, who have expressed willingness to join, on new participants in addition to other context information. Utilizing the participant influence can improve the effectiveness of event recommendation. Experiments on real datasets confirm that the consideration of participant influence can lead to more accurate prediction, offering better event recommendation.

When a user is making a decision on whether to participate an event in Event-based Social Networks (EBSN), one of the common considerations is who have agreed to join this event. The reason is that existing participants of the event 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 paper, we propose an event recommendation model which considers participant influence, exploiting the influence of existing participants, on the decisions of new participants. Specifically, we investigate participant influence in relation to several commonly used contextual aspects of the event based on Poisson factorization. We have conducted extensive experiments on some datasets extracted from a real-world EBSN. The results demonstrate that the consideration of participant influence can improve event recommendation.