  Understanding the social and behavioral forces behind event participation is
not only interesting from the viewpoint of social science, but also has
important applications in the design of personalized event recommender systems.
This paper takes advantage of data from a widely used location-based social
network, Foursquare, to analyze event patterns in three metropolitan cities. We
put forward several hypotheses on the motivating factors of user participation
and confirm that social aspects play a major role in determining the likelihood
of a user to participate in an event. While an explicit social filtering signal
accounting for whether friends are attending dominates the factors, the
popularity of an event proves to also be a strong attractor. Further, we
capture an implicit social signal by performing random walks in a high
dimensional graph that encodes the place type preferences of friends and that
proves especially suited to identify relevant niche events for users. Our
findings on the extent to which the various temporal, spatial and social
aspects underlie users' event preferences lead us to further hypothesize that a
combination of factors better models users' event interests. We verify this
through a supervised learning framework. We show that for one in three users in
London and one in five users in New York and Chicago it identifies the exact
event the user would attend among the pool of suggestions.
