A collective Bayesian Poisson factorization model for cold-start local event recommendation

Event-based social networks (EBSNs), in which organizers publish events to attract other users in local city to attend offline, emerge in recent years and grow rapidly. Due to the large volume of events in EBSNs, event recommendation is essential. A few recent works focus on this task, while almost all the methods need that each event to be recommended should have been registered by some users to attend. Thus they ignore two essential characteristics of events in EBSNs: (1) a large number of new events will be published every day which means many events have few participants in the beginning, (2) events have life cycles which means outdated events should not be recommended. Overall, event recommendation in EBSNs inevitably faces the cold-start problem. In this work, we address the new problem of cold-start local event recommendation in EBSNs. We propose a collective Bayesian Poisson factorization (CBPF) model for handling this problem. CBPF takes recently proposed Bayesian Poisson factorization as its basic unit to model user response to events, social relation, and content text separately. Then it further jointly connects these units by the idea of standard collective matrix factorization model. Moreover, in our model event textual content, organizer, and location information are utilized to learn representation of cold-start events for predicting user response to them. Besides, an efficient coordinate ascent algorithm is adopted to learn the model. We conducted comprehensive experiments on real datasets crawled from EBSNs and the results demonstrate our proposed model is effective and outperforms several alternative methods.