Extracting Rankings for Spatial Keyword Queries from GPS Data

Studies suggest that many search engine queries have local intent. We consider the evaluation of ranking functions important for such queries. The key challenge is to be able to determine the "best" ranking for a query, as this enables evaluation of the results of ranking functions. We propose a model that synthesizes a ranking of points of interest (PoI) for a given query using historical trips extracted from GPS data. To extract trips, we propose a novel PoI assignment method that makes use of distances and temporal information. We also propose a PageRank-based smoothing method to be able to answer queries for regions that are not covered well by trips. We report experimental results on a large GPS dataset that show that the proposed model is capable of capturing the visits of users to PoIs and of synthesizing rankings.