Exploiting semantic annotations for clustering geographic areas and users in location-based social networks

Location-Based Social Networks (LBSN) present so far the most vivid realization of the convergence of the physical and virtual social planes. In this work we propose a novel approach on modeling human activity and geographical areas by means of place categories. We apply a spectral clustering algorithm on areas and users of two metropolitan cities on a dataset sourced from the most vibrant LBSN, Foursquare. Our methodology allows the identification of user communities that visit similar categories of places and the comparison of urban neighborhoods within and across cities. We demonstrate how semantic information attached to places could be plausibly used as a modeling interface for applications such as recommender systems and digital tourist guides. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved