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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; Schreck, Tobias; Veas, Eduardo; Sabon, Vedran; Eggeling, Eva; Fellner, Dieter W.


Association for Computing Machinery -ACM-:
ETRA 2018, ACM Symposium on Eye Tracking Research & Applications. Proceedings : Warsaw, Poland, June 14 - 17, 2018
New York: ACM, 2018
ISBN: 978-1-4503-5706-7
Art. 13, 9 pp.
Symposium on Eye Tracking Research & Applications (ETRA) <2018, Warsaw>
Conference Paper
Fraunhofer IGD ()
evaluation; human-centered computing; visual analytic; recommender systems; eye tracking; Guiding Theme: Digitized Work; Research Area: Human computer interaction (HCI)

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.