Answering Why-Not Group Spatial Keyword Queries
IEEE Transactions on Knowledge and Data Engineering(2020)
摘要
With the proliferation of geo-textual objects on the web, extensive efforts have been devoted to improving the efficiency of top-
$k$ k
spatial keyword queries in different settings. However, comparatively much less work has been reported on enhancing the quality and usability of such queries. In this context, we propose means of enhancing the usability of a top-
$k$ k
group spatial keyword query, where a group of users aim to find
$k$ k
objects that contain given query keywords and are nearest to the users. Specifically, when users receive the result of such a query, they may find that one or more objects that they expect to be in the result are in fact missing, and they may wonder why. To address this situation, we develop a so-called
why-not
query that is able to minimally modify the original query into a query that returns the expected, but missing, objects, in addition to other objects. Specifically, we formalize the
why-not
query in relation to the top-
$k$ k
group spatial keyword query, called the
W
hy-not
G
roup
S
patial
K
eyword Query (
$\mathsf{WGSK}$ WGSK
) that is able to provide a group of users with a more satisfactory query result. We propose a three-phase framework for efficiently computing the
$\mathsf{WGSK}$ WGSK
. The first phase substantially reduces the search space for the subsequent phases by retrieving a set of objects that may affect the ranking of the user-expected objects. The second phase provides an incremental sampling algorithm that generates candidate weightings of more promising queries. The third phase determines the penalty of each refined query and returns the query with minimal penalty, i.e., the minimally modified query. Extensive experiments with real and synthetic data offer evidence that the proposed solution excels over baselines with respect to both effectiveness and efficiency.
更多查看译文
关键词
Usability,Electronic mail,Search problems,Transportation,Aggregates,Indexes
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络