A personalized query method for spatial keywords in indoor environments

Abstract With the rapid development of indoor Location Based Services (LBS), a growing volume of textual data is being generated in indoor environments. Consequently, indoor spatial keyword query holds significant potential for development in the coming years. However, existing methods for indoor spatial keyword queries often neglect the personalized needs of users. To solve this problem, we propose an Indoor Spatial Keyword Personalized Query (ISKPQ) method. First, a novel index structure called ISKIR-tree has been designed. This index integrates Hilbert encoding techniques and introduces Bloom filters and distance matrices to enhance the efficiency of processing indoor spatial keyword problems. Subsequently, an efficient pruning algorithm based on the ISKIR-tree index is proposed to refine the dataset effectively. Finally, a comprehensive scoring function that considers text similarity, spatial proximity, and user preferences is introduced to score and rank the pruned data points, thereby filtering out the optimal query results that meet users’ personalized requirements. Theoretical analysis and experimental studies demonstrate the outstanding performance of the proposed method in terms of both efficiency and accuracy.