Robust and continuous machine learning of usage habits to adapt digital interfaces to user needs

The paper presents a machine learning approach to design digital interfaces that can dynamically adapt to different users and usage strategies. The algorithm uses Bayesian statistics to model users' browsing behavior, focusing on their habits rather than g roup preferences. It is distinguished by its online incremental learning, allowing reliable predictions even with little data and in the case of a changing environment. This inference method generates a task model, providing a graphical representation of n avigation with the usage statistics of the current user. The algorithm learns new tasks while preserving prior knowledge. The theoretical framework is described, and simulations show the effectiveness of the approach in stationary and non - stationary environments. In conclusion, this research paves the way for adaptive systems that improve the user experience by helping them to better navigate and act on their inter face. The reasons given include that it would be too oriented toward machine learning to speak to a community of HCI researchers and not concrete enough, as well as other reasons that we largely dispute. In light of the comments from the two reviewers, it appears that our non - parametric Bayesian approach was not understood, nor the crucial issue of "sequential, continuous and robust learning" for the design of adaptive user interfaces. 2 1 INTRODUCTION Users are all different. Some have no particular constraints but have usage habits and preferences. Others, such as people with disabilities or seniors, may have, in addition to these habits, constraints when using a digital service. These constraints can be very diverse, of a perceptual nature (visual, auditory, tactile), of a motor nature (pointing, manipulation, speech) or cognitive (reasoning, memory, comprehension, reading...). Consequently, any service, any interface should be able to adjust to these constraints.