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Exploring gaze-based prediction strategies for preference detection in dynamic interface elements
Heck, Melanie
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Edinger, Janick
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Bünemann, Jonathan
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Becker, Christian
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DOI:
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https://doi.org/10.1145/3406522.3446013
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URL:
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https://dl.acm.org/doi/abs/10.1145/3406522.3446013
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Additional URL:
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https://crossminds.ai/video/exploring-gaze-based-p...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2021
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Book title:
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CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval : Canberra ACT, Australia, March, 2021
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Page range:
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129-139
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Conference title:
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CHIIR '21: 6th ACM SIGIR Conference
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Location of the conference venue:
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Online
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Date of the conference:
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14.-19.03.2021
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Publisher:
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Scholer, Falk
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Place of publication:
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New York, NY
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Publishing house:
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Association for Computing Machinery
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ISBN:
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978-1-4503-8055-3
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Publication language:
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English
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Institution:
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Business School > Wirtschaftsinformatik II (Becker 2006-2021)
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Subject:
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004 Computer science, internet
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Abstract:
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Digitization is currently infiltrating all daily processes, forcing casual computer users to become acquainted with unfamiliar tools. In order to avoid overstraining these users, simplified interfaces that are reduced to the functionality and content which are relevant to the individual user are imperative. Gaze-contingent systems thus monitor viewing behavior during natural system interactions to predict relevant interface elements. The prediction performance is highly dependent on the underlying features and algorithm, especially when the interface consist of dynamic elements such as videos. In this paper, we conduct two studies with a total of 233 subjects in which we record the viewers' gaze while watching videos. We then compare the quality of preference predictions for video elements of majority voting to the performance of machine learning. Our results indicate that (1) majority voting can predict preferences with an accuracy of up to 73% (66%) for two (four) elements, (2) machine learning improves the performance to 82% (74%), (3) prediction accuracy depends on the strength of the user's preference for an element, and (4) we can rank preferences for individual elements.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Heck, Melanie
;
Edinger, Janick
;
Bünemann, Jonathan
;
Becker, Christian
Google Scholar:
Heck, Melanie
;
Edinger, Janick
;
Bünemann, Jonathan
;
Becker, Christian
ORCID:
Heck, Melanie  ORCID: https://orcid.org/0000-0002-9601-0064, Edinger, Janick, Bünemann, Jonathan and Becker, Christian
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