Towards Why-Not Spatial Keyword Top-$k$ Queries: A Direction-Aware Approach

Abstract

With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. An important query over such web objects is the <i>direction-aware spatial keyword query</i> that aims to retrieve the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top- <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users’ direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.