Inferring and exploiting categories for next location prediction
Predicting the next location of a user based on their previous vis- iting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many dif- ferent aspects of these so-called check-in" profiles of a user have been made use of in this task, including spatial and temporal in- formation of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with informa- tion from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is as- sociated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with pub- licly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.