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Eye-tracking analysis of user behavior in WWW search

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SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 478 - 479

https://doi.org/10.1145/1008992.1009079

Published: 25 July 2004 Publication History

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Abstract

We investigate how users interact with the results page of a WWW search engine using eye-tracking. The goal is to gain insight into how users browse the presented abstracts and how they select links for further exploration. Such understanding is valuable for improved interface design, as well as for more accurate interpretations of implicit feedback (e.g. clickthrough) for machine learning. The following presents initial results, focusing on the amount of time spent viewing the presented abstracts, the total number of abstract viewed, as well as measures of how thoroughly searchers evaluate their results set.

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Joachims, T. Optimizing search engines using clickthrough data. Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002, pp 132--142.

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Salogarvi, J., Kojo, I., Jaana, S., and Kaski, S. Can relevance be inferred from eye movements in information retrieval? In Proceedings of the Workshop on Self-Organizing Maps (WSOM'03), Hibikino, Kitakyushu, Japan, September 2003. pp. 261--266.

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Index Terms

Eye-tracking analysis of user behavior in WWW search
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. World Wide Web
    1. Web applications
    2. Web services

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Information

Published In

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

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: 25 July 2004

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SIGIR04

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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

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https://dl.acm.org/doi/10.1145/3643709
Rus CPoerwawinata GYates Ade Rijke MSerra ESpezzano F(2024)AnnoRank: A Comprehensive Web-Based Framework for Collecting Annotations and Assessing RankingsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679174(5400-5404)Online publication date: 21-Oct-2024
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https://doi.org/10.1142/S021759082450019X
Liu XGuo CYao BSarikaya R(2024)A Self-Learning Framework for Large-Scale Conversational AI SystemsIEEE Computational Intelligence Magazine10.1109/MCI.2024.336397119:2(34-48)Online publication date: 5-Apr-2024
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