Event Recommendation in Event-based Social Networks

With the large number of events published all the time in event-based social networks (EBSN), it has become increasingly difficult for users to find the events that best match their preferences. Recommender systems appear as a natural solution to this problem. However, the event recommendation scenario is quite different from typical recommendation domains (e.g. movies), since there is an intrinsic new item problem involved (i.e. events can not be ”consumed” before their occurrence) and scarce collaborative information. Although some few works have appeared in this area, there is still lacking in the literature an extensive analysis of the different characteristics of EBSN data that can affect the design of event recommenders. In this paper we provide a contribution in this direction, where we investigate and discuss important features of EBSN such as sparsity, events life time, co-participation of users in events and geographic features. We also shed some light on the performance and limitations of several well known recommendation algorithms and combinations of them on real data collected from meetup.com.