research-article

Learning to rank query suggestions for adhoc and diversity search

Authors:

Rodrygo L. T. Santos,

Craig Macdonald,

Iadh OunisAuthors Info & Claims

Information Retrieval, Volume 16, Issue 4

Pages 429 - 451

https://doi.org/10.1007/s10791-012-9211-2

Published: 01 August 2013 Publication History

Abstract

Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search.

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Cited By

Zhou ZZhou XLi MSong YZhang TYan RAl Hasan MXiong L(2022)Personalized Query Suggestion with Searching Dynamic Flow for Online RecruitmentProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557416(2773-2783)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557416
Li RLi JMitra BDiaz FBiega A(2022)Exposing Query Identification for Search TransparencyProceedings of the ACM Web Conference 202210.1145/3485447.3512262(3662-3672)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512262
Erbacher PDenoyer LSoulier LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Interactive Query Clarification and Refinement via User SimulationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531871(2420-2425)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531871
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Index Terms

Learning to rank query suggestions for adhoc and diversity search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image Information Retrieval

Information Retrieval Volume 16, Issue 4

Aug 2013

129 pages

ISSN:1386-4564

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© Springer Science+Business Media, LLC 2012.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2013

Accepted: 03 September 2012

Received: 21 May 2012

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Cited By

Zhou ZZhou XLi MSong YZhang TYan RAl Hasan MXiong L(2022)Personalized Query Suggestion with Searching Dynamic Flow for Online RecruitmentProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557416(2773-2783)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557416
Li RLi JMitra BDiaz FBiega A(2022)Exposing Query Identification for Search TransparencyProceedings of the ACM Web Conference 202210.1145/3485447.3512262(3662-3672)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512262
Erbacher PDenoyer LSoulier LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Interactive Query Clarification and Refinement via User SimulationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531871(2420-2425)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531871
Chen JMao JLiu YYe ZMa WWang CZhang MMa S(2021)A Hybrid Framework for Session Context ModelingACM Transactions on Information Systems10.1145/344812739:3(1-35)Online publication date: 5-May-2021
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Chen JMao JLiu YZhang FZhang MMa S(2021)Towards a Better Understanding of Query Reformulation Behavior in Web SearchProceedings of the Web Conference 202110.1145/3442381.3450127(743-755)Online publication date: 19-Apr-2021
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Yang YMa BTan JDeng HHuang HZheng Z(2021)FINN: Feedback Interactive Neural Network for Intent RecommendationProceedings of the Web Conference 202110.1145/3442381.3450105(1949-1958)Online publication date: 19-Apr-2021
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Zamani HMitra BChen ELueck GDiaz FBennett PCraswell NDumais SHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Analyzing and Learning from User Interactions for Search ClarificationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401160(1181-1190)Online publication date: 25-Jul-2020
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Tamine LMelgarejo JPinel-Sauvagnat K(2020)What Can Task Teach Us About Query Reformulations?Advances in Information Retrieval10.1007/978-3-030-45439-5_42(636-650)Online publication date: 14-Apr-2020
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Jiang JWang WCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)RINProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271808(197-206)Online publication date: 17-Oct-2018
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