Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

使软件系统的用户界面适应使用环境的要求仍然是一项重大挑战，尤其是当用户对适应质量的要求越来越高时。在过去的三十年中，相当多的方法提供了某种形式的建模来支持用户界面适应。然而，在分析这些方法提供的概念、基础知识和用户体验方面存在一个关键问题，即比较它们的优点和缺点。这些方法如此之多，以至于在现有技术中定位新方法具有挑战性。因此，本文 定义了一个用于智能用户界面适配的概念参考框架，其中包含一组对基于模型的用户界面适配有用的概念适配属性。这组属性的目标是了解任何方法，比较各种方法并产生新的适应思路。我们还分析了机器学习技术在这种情况下可以为数据处理和分析提供的机会，并确定一些开放的挑战，以保证最终用户获得适当的用户体验。相关文献和我们在研究和产业合作方面的经验已被用作提出未来可以解决这些挑战的方向的基础。比较各种方法并产生适应的新想法。我们还分析了机器学习技术在这种情况下可以为数据处理和分析提供的机会，并确定一些开放的挑战，以保证最终用户获得适当的用户体验。相关文献和我们在研究和产业合作方面的经验已被用作提出未来可以解决这些挑战的方向的基础。比较各种方法并产生适应的新想法。我们还分析了机器学习技术在这种情况下可以为数据处理和分析提供的机会，并确定一些开放的挑战，以保证最终用户获得适当的用户体验。相关文献和我们在研究和产业合作方面的经验已被用作提出未来可以解决这些挑战的方向的基础。