Silva, Nelson ; Schreck, Tobias ; Veas, Eduardo ; Sabol, Vedran ; Eggeling, Eva ; 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, pp. 1-9,
New York, NY, ACM, ACM Symposium on Eye Tracking Research & Applications (ETRA), Warsaw, Poland, 2018, ISBN 978-1-4503-5706-7,
DOI: 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.

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