Efficient Processing of Spatial Group Preference Queries

POIs (points of interest) as well as users' check-in information and their ratings on POIs have been widely studies in systems providing location-based services. We note that users usually have their own preferences for POI categories and their own network of friends. Therefore, we aim to provide for a group of users a new kind of POI-finding query that considers not only POI preferences of each user but also other aspects of location-based social networks such as users' locations and POI ratings. We name such a new query as Spatial Group Preference (SGP) query. For a group of users, an SGP query returns top-k POIs that are much likely to satisfy the needs of users. Specially, we propose a new evaluation model that considers user preferences for user preferences for POI categories. Based on this model, we develop basic algorithms based on R-tree to evaluate SGP queries. Further, we design a new index structure called CR-tree to accelerate the query performance. We prove that CR-tree has better pruning efficiency than the traditional R-tree. We conduct experiments on a simulation dataset as well as two real datasets with respect to various configurations. The results suggest the efficiency of our proposal.