A New Location-Based Topic Model for Event Attendees Recommendation

Abstract

Event-based social networks (EBSNs) have gained increasing popularity and rapid growth, EBSNs provide services for users to create events and make plan to attend. Developing and creating recommendation models are important and hot issues in EBSNs in recent years, such as event recommendation to users. Although several recommendation models have been proposed, event attendees recommendation models are not fully studied. In this paper, we study the event attendees recommendation problem through empirical experiments. Because of the nature of new events and severe data sparsity in EBSNs, traditional recommender systems work less efficiently for event attendees recommendation problem. To solve this problem, we propose a new location-based topic model that is based on scores of users computed from three major factors extracted from previously attended events, namely content, location and time. The proposed model includes two phases. The first phase uses the topic modeling Latent Dirichlet Allocation (LDA) and Jensen Shannon divergence to compute the similarity of events based on their contents. The spatial and temporal factors are also calculated. The scores of previous events with an upcoming event are computed from a combination of these three factors. Previous events with high scores are selected, then users who are extracted from the selected events are scored by temporal factors of these events in the second phase. Finally, we recommend users with top scores to the upcoming event. A series of experiments were conducted on real data collected from Meetup Event and the results have demonstrated the improvement of our model over baseline methods.