An effective content-based event recommendation model

Event-based social networks (EBSNs) facilitate people to interact with each other by sharing similar interests in online groups or taking part in offline events together. Event recommendation in EBSNs has been studied by many researchers. However, the problem of recommending the event to the top N active-friends of the key user has rarely been studied in EBSNs. In this paper, we propose a new method to solve this problem. In this method, we first construct an association matrix from the content of events and user features. Then, we define a new content-based event recommendation model, which combines the matrix, spatio-temporal relations and user interests to recommend an event to the active-friends of a key user. A series of experiments were conducted on real datasets collected from Meetup, and the comparison results have demonstrated the effectiveness of the new model.