Paper Info
Title
Answering why-not questions on spatial keyword top-k queries
Abstract
Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from a result; and understanding why may aid users in retrieving better results. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. We provide techniques that allow the revision of spatial keyword queries such that their results include one or more desired, but missing objects. In doing so, we adopt a query refinement approach to provide a basic algorithm that reduces the problem to a two-dimensional geometrical problem. To improve performance, we propose an index-based ranking estimation algorithm that prunes candidate results early. Extensive experimental results offer insight into design properties of the proposed techniques and suggest that they are efficient in terms of both running time and I/O cost.
Year
DOI
Venue
2015
10.1109/ICDE.2015.7113291
ICDE
Field
DocType
ISSN
Query optimization,Web search query,Data mining,Query language,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,Ranking (information retrieval),Spatial query,Database
Conference
1084-4627
Citations 
PageRank 
References 
23
0.70
25
Authors
5
Name
Order
Citations
PageRank
Lei Chen16239395.84
Xin Lin2494.32
Haibo Hu3106866.30
Christian S. Jensen4106511129.45
Jianliang Xu52743168.17