Activity event recommendation and attendance prediction

Abstract

ABSTRACT The recommendation problem has been widely studied and researchers are constantly searching for better methods. Recommending events is an even more difficult problem because there is no information such as ratings from past events. In this paper, we introduce a method for recommending activity events: activities hosted by one or more individuals which involve movement: walking, running, cycling, cross-country skiing, and driving to users who have location history such as trajectories, meetings, POI visits, and geo-tagged photos. We tested the method in a real environment in Mopsi platform: http://cs.uef.fi/mopsi/events. Although there are many location-based event recommendation systems in literature, this is to our knowledge the first system that recommends activity events like bicycle and skiing trips. The experiments show that we can predict whether a user is attending the event or not with 80% accuracy, which is significantly better than random chance (50%).