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Article

Detecting dominant locations from search queries

Authors:

Ying LiAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 424 - 431

https://doi.org/10.1145/1076034.1076107

Published: 15 August 2005 Publication History

Abstract

Accurately and effectively detecting the locations where search queries are truly about has huge potential impact on increasing search relevance. In this paper, we define a search query's dominant location (QDL) and propose a solution to correctly detect it. QDL is geographical location(s) associated with a query in collective human knowledge, i.e., one or few prominent locations agreed by majority of people who know the answer to the query. QDL is a subjective and collective attribute of search queries and we are able to detect QDLs from both queries containing geographical location names and queries not containing them. The key challenges to QDL detection include false positive suppression (not all contained location names in queries mean geographical locations), and detecting implied locations by the context of the query. In our solution, a query is recursively broken into atomic tokens according to its most popular web usage for reducing false positives. If we do not find a dominant location in this step, we mine the top search results and/or query logs (with different approaches discussed in this paper) to discover implicit query locations. Our large-scale experiments on recent MSN Search queries show that our query location detection solution has consistent high accuracy for all query frequency ranges.

References

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Cited By

Peikos GPasi G(2024)A systematic review of multidimensional relevance estimation in information retrievalWIREs Data Mining and Knowledge Discovery10.1002/widm.154114:5Online publication date: 7-May-2024
https://doi.org/10.1002/widm.1541
Fang LKou ZYang YLi T(2023)Representing Spatial Data with Graph Contrastive LearningRemote Sensing10.3390/rs1504088015:4(880)Online publication date: 5-Feb-2023
https://doi.org/10.3390/rs15040880
Guo JLan Y(2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_2
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Index Terms

Detecting dominant locations from search queries
1. Information systems
  1. Information retrieval

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Information

Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2005

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SIGIR

SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Citations

Cited By

Peikos GPasi G(2024)A systematic review of multidimensional relevance estimation in information retrievalWIREs Data Mining and Knowledge Discovery10.1002/widm.154114:5Online publication date: 7-May-2024
https://doi.org/10.1002/widm.1541
Fang LKou ZYang YLi T(2023)Representing Spatial Data with Graph Contrastive LearningRemote Sensing10.3390/rs1504088015:4(880)Online publication date: 5-Feb-2023
https://doi.org/10.3390/rs15040880
Guo JLan Y(2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_2
Hamzei ELi HVasardani MBaldwin TWinter STomko M(2019)Place Questions and Human-Generated Answers: A Data Analysis ApproachGeospatial Technologies for Local and Regional Development10.1007/978-3-030-14745-7_1(3-19)Online publication date: 16-Apr-2019
https://doi.org/10.1007/978-3-030-14745-7_1
Suel T(2018)Geo-Targeted Web SearchEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_176(1625-1630)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_176
Hu YKang CTang JYin DChang Y(2017)Large-Scale Location Prediction for Web PagesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.270263129:9(1902-1915)Online publication date: 1-Sep-2017
https://doi.org/10.1109/TKDE.2017.2702631
Sanderson M(2017)Retrieval Algorithms, SpatialEncyclopedia of GIS10.1007/978-3-319-17885-1_1125(1767-1774)Online publication date: 12-May-2017
https://doi.org/10.1007/978-3-319-17885-1_1125
Whiting SAlonso OPerego RSebastiani FAslam JRuthven IZobel J(2016)SGT FrameworkProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914743(1001-1004)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914743
Dan OParikh VDavison BBennett PJosifovski VNeville JRadlinski F(2016)Improving IP Geolocation using Query LogsProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835820(347-356)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2835776.2835820
Chao Z(2016)From query analysis to user information needs: a study of campus map searchesLibrary Hi Tech10.1108/LHT-12-2014-011034:1(104-129)Online publication date: 21-Mar-2016
https://doi.org/10.1108/LHT-12-2014-0110
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