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.
ACM Symposium on Eye Tracking Research & Applications (ETRA). Warsaw, Poland (14.06.2018-17.06.2018)
doi: 10.1145/3204493.3204546
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Silva, Nelson ; Schreck, Tobias ; Veas, Eduardo ; Sabol, Vedran ; Eggeling, Eva ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | New York, NY |
| Verlag: | ACM |
| Buchtitel: | Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications - ETRA '18 |
| Veranstaltungstitel: | ACM Symposium on Eye Tracking Research & Applications (ETRA) |
| Veranstaltungsort: | Warsaw, Poland |
| Veranstaltungsdatum: | 14.06.2018-17.06.2018 |
| DOI: | 10.1145/3204493.3204546 |
| URL / URN: | https://doi.org/10.1145/3204493.3204546 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Evaluation, Human-centered computing, Visual analytics, Recommender systems, Eye tracking |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 01 Jul 2019 08:46 |
| Letzte Änderung: | 03 Jul 2024 10:45 |
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