Toward the new item problem: Context-enhanced event recommendation in event-based social networks

Abstract

Increasing popularity of event-based social networks (EBSNs) calls for the developments in event recommendation techniques. However, events are uniquely different from conventional recommended items because every event to be recommended is a new item. Traditional recommendation methods such as collaborative filtering techniques, which rely on users’ rating histories, are not suitable for this problem. In this paper, we propose a novel context-enhanced event recommendation method, which exploits the rich context in EBSNs by unifying content, social and geographical information. Experiments on a real-world dataset show promising results of the proposed method.