One problem facing designers of interactive systems is catering to the wide range of users who will use a particular application. Understanding the user is critical to designing a usable interface. There are a number of ways of addressing this problem, including improved design methodologies using ''intuitive'' interface styles, adaptive interfaces, and better training and user support materials. In this article, we argue that each of these solutions involves pattern recognition in one form or another and that machine learning can therefore aid designers of interactive systems in these areas. We report on experiments that demonstrate the potential of machine learning to user modeling that has application to two of these areas in particular: adaptive systems and design methodologies.
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https://www.researchgate.net/publication/233644932_Machine_learning_A_tool_to_support_usability
