Spatial-aware interest group queries in location-based social networks

With the recent advances in positioning and smartphone technologies, a number of social networks such as Twitter, Foursquare and Facebook are acquiring the dimension of location, thus bridging the gap between the physical world and online social networking services. Most of the location-based social networks released check-in services that allow users to share their visiting locations with their friends. In this paper, users' interests are modeled by check-in actions. We propose a new type of Spatial-aware Interest Group (SIG) query that retrieves a user group of size k where each user is interested in the query keywords and they are close to each other in the Euclidean space. We prove that the SIG query problem is NP-complete. A family of efficient algorithms based on the IR-tree is thus proposed for the processing of SIG queries. Experiments on two real datasets show that our proposed algorithms achieve orders of magnitude improvement over the baseline algorithm.