A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation
Wei Zhang, Jianyong Wang · 2015
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every day which means many events have few participants in the beginning, (2) events have life cycles which means outdated events should not be recommended.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.