Nazemi, Kawa ; Retz, Wilhelm ; Kohlhammer, Jörn ; Kuijper, Arjan (2014)
User Similarity and Deviation Analysis for Adaptive Visualizations.
Human Interface and the Management of Information. Proceedings Part I.
doi: 10.1007/978-3-319-07731-4_7
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aim to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Autor(en): | Nazemi, Kawa ; Retz, Wilhelm ; Kohlhammer, Jörn ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | User Similarity and Deviation Analysis for Adaptive Visualizations |
| Sprache: | Englisch |
| Publikationsjahr: | 2014 |
| Verlag: | Springer, Berlin, Heidelberg, New York |
| Reihe: | Lecture Notes in Computer Science (LNCS); 8521 |
| Veranstaltungstitel: | Human Interface and the Management of Information. Proceedings Part I |
| DOI: | 10.1007/978-3-319-07731-4_7 |
| Kurzbeschreibung (Abstract): | Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aim to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Adaptive information visualization, Machine learning, User modeling, Semantics visualization |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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