Semantic-Enhanced and Context-Aware Hybrid Collaborative Filtering for Event Recommendation in Event-Based Social Networks

The fast development of event-based social networks (EBSN) provides a convenient platform for recruiting offline participants via online event announcements. Given its ever-increasing new events, how to accurately recommend users their most preferred ones is a key to the success of an EBSN. In this paper, we propose a semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation, which combines semantic content analysis and contextual event influence for user neighborhood selection. In particular, we first exploit the latent topic model for analyzing event description text and establish each user a long-term interest model and short-term interest model from her event registration history. We next establish each event an influence weight to jointly represent its social impact among users and its semantic uniqueness among events. For one user, we select her neighbors according to their long-term interest similarities weighted by events' influences. For new event recommendation, we construct a user-event rating matrix based on users' short-term interest models and for each user, we compute event rating predictions from her neighbors' ratings. The experiments based on the real-world dataset demonstrate the superiority of our algorithm over the peer schemes.