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.

摘要

使用户界面（UI）适应不断变化的使用环境旨在支持交互效果并维持UI可用性。但是，在设计时设计和/或处理UI适应不包括实际情况要求。就情况和周围环境而言，适应应该对软件模式和外观产生跨领域的低成本影响。为了满足此要求，我们提出了基于运行时自适应和自适应UI的用户反馈和机器学习的TADAP建议。它允许在运行时通过混合启动对用户界面（UI）进行任务驱动的调整。TADAP的特殊性是利用机器学习潜力来支持模型驱动的UI中的上下文感知的运行时自适应。进一步，TADAP允许通过在交互过程中考虑用户偏好（隐式和显式）的混合启动方式（用户和系统）来进行UI调整。这种混合启动的运行时UI自适应工具提供了有关如何个性化UI的建议。此外，它具有跟踪实时用户干预并了解其偏好的能力。进行了各种测试，并显示出TADAP是一种有前途的主动性，可用于智能模型驱动的UI调整。