Spatial-Temporal Topic Model for Cold-Start Event Recommendation

Event recommendation has attracted an increasing attention with the popularity of event-based social networks (EBSNs). The previous studies mainly focus on exploiting various contextual information to alleviate the cold-start problem in event recommendation. However, the interactions between different contextual factors, such as time, location and content, have not been modeled jointly. In this paper, we investigate the relationships among time, location and organizer in EBSNs, and propose a Spatial-Temporal Topic Model (STTM) for cold-start event recommendation. STTM can capture user's interests on content and geographical space changing over time, and users have different event and venue topic distributions at different times in STTM. We perform an experimental evaluation on the real-world EBSNs dataset. The experimental results show the significant improvements of our method over other comparison methods, especially when dataset is more sparse.