Toward a Task-driven Intelligent GUI Adaptation by Mixed-initiative.
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| Title: | Toward a Task-driven Intelligent GUI Adaptation by Mixed-initiative. |
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| Authors: | Mezhoudi, Nesrine (AUTHOR), Vanderdonckt, Jean (AUTHOR) |
| Source: | International Journal of Human-Computer Interaction. Mar2021, Vol. 37 Issue 5, p445-458. 14p. 2 Color Photographs, 3 Diagrams, 6 Charts, 3 Graphs. |
| Subjects: | Machine learning, User interfaces, Technical specifications |
| Abstract: | Adapting the user interface (UI) to the changing context of use is intended to support the interaction effectiveness and sustain UI usability. However, designing and/or processing UIs adaptation at design time does not encompass real situation requirements. Adaptation should have a cross-cutting and low-cost impact on software patterning and appearance with regard to the situation and the ambient-context. To meet this requirement, we present TADAP proposal for run-time adaptive and adaptable UI based user feedbacks and machine learning. It allows a task-driven adaptation of the user interface (UI) at runtime by mixed-initiative. The particularity of TADAP is the utilization of Machine Learning potential to support context-aware runtime adaptation within model-driven UI. Further, TADAP allows the UI adaptation by mixed-initiative (User and System) considering the user preferences (implicit and explicit) during an interaction. Such a mixed-initiative runtime UI-adaptation tool provides recommendations on how to personalize the UI. Further, it has the ability to track real-time users' interventions and learn their preferences. Diverse tests were performed and showed TADAP as a promising initiative for intelligent model-driven UI adaptation. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Adapting the user interface (UI) to the changing context of use is intended to support the interaction effectiveness and sustain UI usability. However, designing and/or processing UIs adaptation at design time does not encompass real situation requirements. Adaptation should have a cross-cutting and low-cost impact on software patterning and appearance with regard to the situation and the ambient-context. To meet this requirement, we present TADAP proposal for run-time adaptive and adaptable UI based user feedbacks and machine learning. It allows a task-driven adaptation of the user interface (UI) at runtime by mixed-initiative. The particularity of TADAP is the utilization of Machine Learning potential to support context-aware runtime adaptation within model-driven UI. Further, TADAP allows the UI adaptation by mixed-initiative (User and System) considering the user preferences (implicit and explicit) during an interaction. Such a mixed-initiative runtime UI-adaptation tool provides recommendations on how to personalize the UI. Further, it has the ability to track real-time users' interventions and learn their preferences. Diverse tests were performed and showed TADAP as a promising initiative for intelligent model-driven UI adaptation. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10447318 |
| DOI: | 10.1080/10447318.2020.1824742 |
