research-article

Directing exploratory search with interactive intent modeling

Authors:

Kumaripaba Athukorala,

Samuel KaskiAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1759 - 1764

https://doi.org/10.1145/2505515.2505644

Published: 27 October 2013 Publication History

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Abstract

We introduce interactive intent modeling, where the user directs exploratory search by providing feedback for estimates of search intents. The estimated intents are visualized for interaction on an Intent Radar, a novel visual interface that organizes intents onto a radial layout where relevant intents are close to the center of the visualization and similar intents have similar angles. The user can give feedback on the visualized intents, from which the system learns and visualizes improved intent estimates. We systematically evaluated the effect of the interactive intent modeling in a mixed-method task-based information seeking setting with 30 users, where we compared two interface variants for interactive intent modeling, namely intent radar and a simpler list-based interface, to a conventional search system. The results show that interactive intent modeling significantly improves users' task performance and the quality of retrieved information.

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Cited By

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Wenskovitch JDowling MNorth C(2024)Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual AnalyticsACM Transactions on Interactive Intelligent Systems10.1145/358856514:1(1-35)Online publication date: 9-Jan-2024
https://dl.acm.org/doi/10.1145/3588565
Zhang YLiu JLai CZhou YChen S(2023)Interpreting High-Dimensional Projections With CapacityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332485130:9(6038-6055)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3324851
Sultanum NBezerianos AChevalier F(2021)Text Visualization and Close Reading for Journalism with Storifier2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623264(186-190)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623264
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Index Terms

Directing exploratory search with interactive intent modeling
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Citations

Cited By

View all

Wenskovitch JDowling MNorth C(2024)Toward Addressing Ambiguous Interactions and Inferring User Intent with Dimension Reduction and Clustering Combinations in Visual AnalyticsACM Transactions on Interactive Intelligent Systems10.1145/358856514:1(1-35)Online publication date: 9-Jan-2024
https://dl.acm.org/doi/10.1145/3588565
Zhang YLiu JLai CZhou YChen S(2023)Interpreting High-Dimensional Projections With CapacityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332485130:9(6038-6055)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1109/TVCG.2023.3324851
Sultanum NBezerianos AChevalier F(2021)Text Visualization and Close Reading for Journalism with Storifier2021 IEEE Visualization Conference (VIS)10.1109/VIS49827.2021.9623264(186-190)Online publication date: Oct-2021
https://doi.org/10.1109/VIS49827.2021.9623264
Strahl JPeltonen JFloréen P(2021)Directing and Combining Multiple Queries for Exploratory Search by Visual Interactive Intent ModelingHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85613-7_34(514-535)Online publication date: 30-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-85613-7_34
di Sciascio CVeas EBarria-Pineda JCulley CPaternò FOliver NConati CSpano LTintarev N(2020)Understanding the effects of control and transparency in searching as learningProceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377524(498-509)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377524
Wenskovitch JDowling MNorth CPaternò FOliver NConati CSpano LTintarev N(2020)With respect to what?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377516(177-188)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377516
Vakkari PO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)The Usefulness of Search ResultsProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3377955(243-252)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3377955
Pereira MPaulovich F(2020)RankViz: A visualization framework to assist interpretation of Learning to Rank algorithmsComputers & Graphics10.1016/j.cag.2020.09.01793(25-38)Online publication date: Dec-2020
https://doi.org/10.1016/j.cag.2020.09.017
Ruotsalo TJacucci GKaski S(2020)Interactive faceted query suggestion for exploratory searchJournal of the Association for Information Science and Technology10.1002/asi.2430471:7(742-756)Online publication date: 8-Jun-2020
https://dl.acm.org/doi/10.1002/asi.24304
Filetti MBarral OJacucci GRavaja N(2019)Motivational intensity and visual word search: Layout mattersPLOS ONE10.1371/journal.pone.021892614:7(e0218926)Online publication date: 23-Jul-2019
https://doi.org/10.1371/journal.pone.0218926
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