Event participation recommendation in event-based social networks

Abstract

Event-based Social Networks (EBSN) have experienced rapid growth in recent years. Event participation recommendation is to recommend a list of users who are most likely to participate in a new event. Due to the nature of new event and severe data sparsity in EBSN, the traditional recommender systems do not work well for event participation recommendation. In this paper, we first conduct a study of Meetup users to understand the major factors impacting their event participation decisions. We then develop a sliding-window based machine-learning model that effectively combines user features from multiple channels to recommend users to new events. Through evaluation using the Meetup dataset, we demonstrate that our model can capture the short-term consistency of user preferences and outperforms the traditional popularitybased and nearest-neighbor based recommendation models. Our model is suitable for real-time recommendation on practical EBSN platforms.