Exploring gaze-based prediction strategies for preference detection in dynamic interface elements

Heck, Melanie ; Edinger, Janick ; Bünemann, Jonathan ; Becker, Christian

DOI: https://doi.org/10.1145/3406522.3446013
URL: https://dl.acm.org/doi/abs/10.1145/3406522.3446013
Additional URL: https://crossminds.ai/video/exploring-gaze-based-p...
Document Type: Conference or workshop publication
Year of publication: 2021
Book title: CHIIR '21: ACM SIGIR Conference on Human Information Interaction and Retrieval : Canberra ACT, Australia, March, 2021
Page range: 129-139
Conference title: CHIIR '21: 6th ACM SIGIR Conference
Location of the conference venue: Online
Date of the conference: 14.-19.03.2021
Publisher: Scholer, Falk
Place of publication: New York, NY
Publishing house: Association for Computing Machinery
ISBN: 978-1-4503-8055-3
Publication language: English
Institution: Business School > Wirtschaftsinformatik II (Becker 2006-2021)
Subject: 004 Computer science, internet
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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