Social space keyword query based on semantic trajectory

摘要

With the booming development of smart phones and location-based services, spatial keyword queries of semantic trajectories can take location information and multiple keywords as parameters and return semantic trajectories related to these parameters in space and text. With the development of the Location Based Social Network (LBSN) in recent years, social data can be used to improve query results. Generally, the semantic trajectory of having more supporters (users with positive comments about the location) who are more closely related to the query user is more likely to be the recommended result. Considering social information in the query can recommend more satisfactory results for users, this paper studies a new problem of semantic trajectory space keyword query: Social space Keyword Query based on semantic Trajectory (SKQT). This paper proposes a hybrid index structure that introduces social factors called Social Inverted Linear Quadtree (SIL-Quadtree) to improve the efficiency of SKQT query. The experiments use two datasets, Gowalla and Brightkite. The results show that the algorithm and index structure proposed in this paper perform better on running time and I/O. (C) 2020 Elsevier B.V. All rights reserved.