Inferring and Exploiting Categories for Next Location Prediction

Ankita Likhyani, Deepak Padmanabhan, Srikanta Bedathur, Sameep Mehta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information 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 information 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 associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.
Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on World Wide Web Companion, WWW 2015: Companion Volume.
Number of pages2
ISBN (Electronic)9781450334730
Publication statusPublished - 2015
Event24th International Conference on World Wide Web Companion, WWW 2015 - Florence, Italy
Duration: 18 May 201422 May 2015


Conference24th International Conference on World Wide Web Companion, WWW 2015


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