Silva, NelsonNelsonSilvaSchreck, TobiasTobiasSchreckVeas, EduardoEduardoVeasSabon, VedranVedranSabonEggeling, EvaEvaEggelingFellner, Dieter W.Dieter W.Fellner2022-03-142022-03-142018https://publica.fraunhofer.de/handle/publica/40328710.1145/3204493.3204546We 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.enevaluationhuman-centered computingvisual analyticrecommender systemseye trackingLead Topic: Digitized WorkResearch Line: Human computer interaction (HCI)006conference paper