Learn to Recommend Local Event Using Heterogeneous Social Networks

Event-based social networks (EBSNs), which link the online and offline social networks, are increasing popular online services. Along with dramatic rise of the users and events in EBSNs, it is necessary to recommend event to users. Taking full advantage of social networks information can significantly improve predictive accuracy in recommender systems. The intuition here is that the user’s response to events are determined by his/her instinct and behaviours of friends. We propose a Heterogeneous Social Poisson Factorization(HSPF) model which combines online and offline social networks into one framework, and integrates the tie strength of online and offline friend relationships to the model. We test HSPF on Meetup dataset. Experimental results demonstrate that HSPF outperforms state-of-the-art recommendation methods.