research-article

Utilizing query change for session search

Authors:

Hui YangAuthors Info & Claims

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Pages 453 - 462

https://doi.org/10.1145/2484028.2484055

Published: 28 July 2013 Publication History

Abstract

Session search is the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the information need. This paper proposes a novel query change retrieval model (QCM), which utilizes syntactic editing changes between adjacent queries as well as the relationship between query change and previously retrieved documents to enhance session search. We propose to model session search as a Markov Decision Process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent's actions are query changes that we observe and the search agent's actions are proposed in this paper. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and 2012.

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Cited By

Zhan JAi QLiu YChen JMa SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Capability-aware Prompt Reformulation Learning for Text-to-Image GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657787(2145-2155)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657787
Liu JLiu J(2023)Formally Modeling Users in Information RetrievalA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_2(23-64)Online publication date: 18-Feb-2023
https://doi.org/10.1007/978-3-031-23229-9_2
Labhishetty SZhai CCrestani FPasi GGaussier E(2022)PRE: A Precision-Recall-Effort Optimization Framework for Query SimulationProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545136(51-60)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545136
Show More Cited By

Index Terms

Utilizing query change for session search
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

July 2013

1188 pages

ISBN:9781450320344

DOI:10.1145/2484028

General Chairs:
Gareth J.F. Jones
Dublin City University, Ireland
,
Páraic Sheridan
Dublin City University, Ireland
,
Program Chairs:
Diane Kelly
University of North Carolina, Chapel Hill, USA
,
Maarten de Rijke
University of Amsterdam, The Netherlands
,
Tetsuya Sakai
Microsoft Research Asia, China

Copyright © 2013 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: 28 July 2013

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SIGIR '13

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SIGIR

SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval

July 28 - August 1, 2013

Dublin, Ireland

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SIGIR '13 Paper Acceptance Rate 73 of 366 submissions, 20%;

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

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Cited By

Zhan JAi QLiu YChen JMa SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Capability-aware Prompt Reformulation Learning for Text-to-Image GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657787(2145-2155)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657787
Liu JLiu J(2023)Formally Modeling Users in Information RetrievalA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_2(23-64)Online publication date: 18-Feb-2023
https://doi.org/10.1007/978-3-031-23229-9_2
Labhishetty SZhai CCrestani FPasi GGaussier E(2022)PRE: A Precision-Recall-Effort Optimization Framework for Query SimulationProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545136(51-60)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545136
Zuo XDou ZWen JSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Improving Session Search by Modeling Multi-Granularity Historical Query ChangeProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498415(1534-1542)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498415
Breuer TFuhr NSchaer P(2022)Validating Simulations of User Query VariantsAdvances in Information Retrieval10.1007/978-3-030-99736-6_6(80-94)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99736-6_6
Zhao JHuang JDeng HChang YXia L(2021)Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search PersonalizationACM Transactions on Information Systems10.1145/347610640:3(1-24)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3476106
Chen JLiu YMao JZhang FSakai TMa WZhang MMa SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Incorporating Query Reformulating Behavior into Web Search EvaluationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482438(171-180)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482438
Zhu YNie JDou ZMa ZZhang XDu PZuo XJiang HDemartini GZuccon GCulpepper JHuang ZTong H(2021)Contrastive Learning of User Behavior Sequence for Context-Aware Document RankingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482243(2780-2791)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482243
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
https://dl.acm.org/doi/10.1145/3448127
Wang PHou YLi ZZhang Y(2021)QIRM: A quantum interactive retrieval model for session searchNeurocomputing10.1016/j.neucom.2021.04.013451(57-66)Online publication date: Sep-2021
https://doi.org/10.1016/j.neucom.2021.04.013
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