research-article

Adaptive query suggestion for difficult queries

Authors:

Ji-Rong WenAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 15 - 24

https://doi.org/10.1145/2348283.2348289

Published: 12 August 2012 Publication History

Abstract

Query suggestion is a useful tool to help users formulate better queries. Although this has been found highly useful globally, its effect on different queries may vary. In this paper, we examine the impact of query suggestion on queries of different degrees of difficulty. It turns out that query suggestion is much more useful for difficult queries than easy queries. In addition, the suggestions for difficult queries should rely less on their similarity to the original query. In this paper, we use a learning-to-rank approach to select query suggestions, based on several types of features including a query performance prediction. As query suggestion has different impacts on different queries, we propose an adaptive suggestion approach that makes suggestions only for difficult queries. We carry out experiments on real data from a search engine. Our results clearly indicate that an approach targeting difficult queries can bring higher gain than a uniform suggestion approach.

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Cited By

Alaofi MGallagher LMckay DSaling LSanderson MScholer FSpina DWhite RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Where Do Queries Come From?Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531711(2850-2862)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531711
Reddy RVaishnavi CMamatha CAnanthakumaran S(2021)Location‐Aware Keyword Query Suggestion Techniques With Artificial Intelligence PerspectiveComputational Analysis and Deep Learning for Medical Care10.1002/9781119785750.ch2(35-51)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119785750.ch2
Li LDeng HChang Y(2020)Query Auto-CompletionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_7(145-170)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_7
Show More Cited By

Index Terms

Adaptive query suggestion for difficult queries
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 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: 12 August 2012

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SIGIR '12

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SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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

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Cited By

Alaofi MGallagher LMckay DSaling LSanderson MScholer FSpina DWhite RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Where Do Queries Come From?Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531711(2850-2862)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531711
Reddy RVaishnavi CMamatha CAnanthakumaran S(2021)Location‐Aware Keyword Query Suggestion Techniques With Artificial Intelligence PerspectiveComputational Analysis and Deep Learning for Medical Care10.1002/9781119785750.ch2(35-51)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119785750.ch2
Li LDeng HChang Y(2020)Query Auto-CompletionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_7(145-170)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_7
Liao ZTajima K(2019)Disjunctive Sets of Phrase Queries for Diverse Query SuggestionIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352566(449-455)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352566
LIU YLiu YZhou KZhang MMa SYin YLuo HMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Detecting Promotion Campaigns in Query Auto CompletionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983709(125-134)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983709
Chen ZYamamoto TTanaka K(2016)Query Suggestion for Struggling Search by Struggling Flow Graph2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0040(224-231)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0040
Qi SWu DMamoulis N(2016)Location Aware Keyword Query Suggestion Based on Document ProximityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.246539128:1(82-97)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2465391
Dileep KChakraborti S(2016)Eager to be Lazy: Towards a Complexity-guided Textual Case-Based Reasoning SystemCase-Based Reasoning Research and Development10.1007/978-3-319-47096-2_6(77-92)Online publication date: 29-Sep-2016
https://doi.org/10.1007/978-3-319-47096-2_6
Wang JZhexue Huang JGuo JLan Y(2015)Recommending high-utility search engine queries via a query-recommending modelNeurocomputing10.1016/j.neucom.2015.04.076167:C(195-208)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1016/j.neucom.2015.04.076
Wang JHuang JGuo JLan Y(2015)Query ranking model for search engine query recommendationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-015-0362-58:3(1019-1038)Online publication date: 3-May-2015
https://doi.org/10.1007/s13042-015-0362-5
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