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The location check-ins of users through various location-based services suchas Foursquare, Twitter, and Facebook Places, etc., generate large traces ofgeo-tagged events. These event-traces often manifest in hidden (possiblyoverlapping) communities of users with similar interests. Inferring theseimplicit communities is crucial for forming user profiles for improvements inrecommendation and prediction tasks. Given only time-stamped geo-tagged tracesof users, can we find out these implicit communities, and characteristics ofthe underlying influence network? Can we use this network to improve the nextlocation prediction task? In this paper, we focus on the problem of communitydetection as well as capturing the underlying diffusion process and propose amodel COLAB based on Spatio-temporal point processes in continuous time butdiscrete space of locations that simultaneously models the implicit communitiesof users based on their check-in activities, without making use of their socialnetwork connections. COLAB captures the semantic features of the location,user-to-user influence along with spatial and temporal preferences of users. Tolearn the latent community of users and model parameters, we propose analgorithm based on stochastic variational inference. To the best of ourknowledge, this is the first attempt at jointly modeling the diffusion processwith activity-driven implicit communities. We demonstrate COLAB achieves up to27% improvements in location prediction task over recent deep point-processbased methods on geo-tagged event traces collected from Foursquare check-ins.

Modeling Implicit Communities using Spatio-Temporal Point Processes from Geo-tagged Event Traces