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Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis

Silva, Nelson and Schreck, Tobias and Veas, Eduardo and Sabol, Vedran and Eggeling, Eva and Fellner, Dieter W. (2018):
Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis.
In: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications - ETRA '18, New York, NY, ACM, In: ACM Symposium on Eye Tracking Research & Applications (ETRA), Warsaw, Poland, 2018, ISBN 978-1-4503-5706-7,
DOI: 10.1145/3204493.3204546,
[Online-Edition: https://doi.org/10.1145/3204493.3204546],
[Conference or Workshop Item]

Abstract

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Silva, Nelson and Schreck, Tobias and Veas, Eduardo and Sabol, Vedran and Eggeling, Eva and Fellner, Dieter W.
Title: Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis
Language: English
Abstract:

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.

Title of Book: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications - ETRA '18
Place of Publication: New York, NY
Publisher: ACM
ISBN: 978-1-4503-5706-7
Uncontrolled Keywords: Evaluation, Human-centered computing, Visual analytics, Recommender systems, Eye tracking
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: ACM Symposium on Eye Tracking Research & Applications (ETRA)
Event Location: Warsaw, Poland
Event Dates: 2018
Date Deposited: 01 Jul 2019 08:46
DOI: 10.1145/3204493.3204546
Official URL: https://doi.org/10.1145/3204493.3204546
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