Efficient Processing of Location-Aware Group Preference Queries

With the proliferation of geo-positioning techniques that enable users to acquire their geographical positions, there has been increasing popularity of online location-based services. This development has generated a large volume of points of interest labeled with category features (e.g., hotel, resort, stores, stations, and tourist attractions). It gives prominence to various types of spatial-keyword queries, which are employed to provide fundamental querying functionality for location-based services. 

We study the Location-aware Group Preference (LGP) query that aims to find a destination place for a group of users. The group of users want to go to a place labeled with a specified category feature (e.g., hotel) together, and each of them has a location and a set of additional preferences. It is expected that the result place of the query belongs to the specified category feature, and it is close to places satisfying the preferences of each user. We develop a novel framework for answering the LGP query, which can be used to compute both exact query result and approximate result with a proven approximation ratio. The efficiency and efficacy of the proposed algorithms for answering the LGP query are verified by extensive experiments on two real datasets.