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Predicting Information Seeking Intentions from Search Behaviors

Authors:

Matthew Mitsui,

Jiqun Liu,

Nicholas J. Belkin,

Chirag ShahAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1121 - 1124

https://doi.org/10.1145/3077136.3080737

Published: 07 August 2017 Publication History

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Abstract

It has been shown that people attempt to accomplish a variety of intentions during the course of an information seeking session, and there is reason to believe that these different information seeking intentions can benefit from system support tailored to each such intention. We address the problem of predicting the presence of such intentions during an information seeking session, through analysis of observable user search behaviors. We present results of a study of 40 participants, each working on two different journalism tasks, which investigated how their search behaviors could indicate their intentions. Using 725 query-segments captured from this study, we demonstrate that information seeking intentions can be predicted with a simple classification model using a linear combination of search behavior features that can be logged with a browser plug-in.

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Cited By

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Zhu RCheng CSong YCai Z(2024)Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information SeekingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997508:4(1-28)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699750
Azzopardi LLiu JSakai TIshita EOhshima HHasibi FMao JJose J(2024)Evaluating Cognitive Biases in Conversational and Generative IIR: A TutorialProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698437(287-290)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698437
Liu JAzzopardi LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Search under Uncertainty: Cognitive Biases and Heuristics: A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search ExperimentsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661382(3013-3016)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661382
Show More Cited By

Index Terms

Predicting Information Seeking Intentions from Search Behaviors
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query intent
    2. Users and interactive retrieval
      1. Task models

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Information & Contributors

Information

Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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Short-paper

Funding Sources

National Science Foundation

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

Acceptance Rates

SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

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

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Citations

Cited By

View all

Zhu RCheng CSong YCai Z(2024)Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information SeekingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997508:4(1-28)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699750
Azzopardi LLiu JSakai TIshita EOhshima HHasibi FMao JJose J(2024)Evaluating Cognitive Biases in Conversational and Generative IIR: A TutorialProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698437(287-290)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698437
Liu JAzzopardi LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Search under Uncertainty: Cognitive Biases and Heuristics: A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search ExperimentsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661382(3013-3016)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3661382
Jung YLiu J(2024)Toward a conceptual framework characterizing the interplay of interest development, information search, and knowledge construction (ISK) in Children’s learningAslib Journal of Information Management10.1108/AJIM-01-2024-0041Online publication date: 24-Jun-2024
https://doi.org/10.1108/AJIM-01-2024-0041
Sarkar SAmirizaniani MShah C(2023)Representing Tasks with a Graph-Based Method for Supporting Users in Complex Search TasksProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578279(378-382)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578279
Marvin Jörs JWilliam De Luca E(2023)Predictive Behavior Modeling Through Web Graphs: Enhancing Next Page Prediction Using Dynamic Link Repository2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00068(415-420)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00068
Danemayer JHolloway CCho YBerthouze NSingh ABhot WDixon OGrobelnik MShawe-Taylor J(2023)Seeking information about assistive technologyInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103078177:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2023.103078
Liu JLiu J(2023)Behavioral Economics in IRA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_6(155-180)Online publication date: 18-Feb-2023
https://doi.org/10.1007/978-3-031-23229-9_6
Liu JLiu J(2023)Back to the Fundamentals: Extend the Rational AssumptionsA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_5(131-152)Online publication date: 18-Feb-2023
https://doi.org/10.1007/978-3-031-23229-9_5
Liu JLiu J(2023)From Rational Agent to Human with Bounded RationalityA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_3(65-89)Online publication date: 18-Feb-2023
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Recommendations

Extracting Information Seeking Intentions for Web Search Sessions

Task, Information Seeking Intentions, and User Behavior: Toward A Multi-level Understanding of Web Search

Post-Acceptance Intentions and Behaviors: An Empirical Investigation of Information Technology Use and Innovation