Addressing the cold-start problem in location recommendation using geo-social correlations

Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their socio-spatial behavior, in order to improve location-based applications like location recommendation. As users can check-in at new places, traditional work on location prediction that relies on mining a user's historical moving trajectories fails as it is not designed for the cold-start problem of recommending new check-ins. While previous work on LBSNs attempting to utilize a user's social connections for location recommendation observed limited help from social network information. In this work, we propose to address the cold-start location recommendation problem by capturing the correlations between social networks and geographical distance on LBSNs with a geo-social correlation model. The experimental results on a real-world LBSN dataset demonstrate that our approach properly models the geo-social correlations of a user's cold-start check-ins and significantly improves the location recommendation performance.