Robust and continuous machine learning of usage habits to adapt digital interfaces to user needs
This work addresses the need for personalized user interfaces that improve navigation and experience, though it appears incremental in applying Bayesian methods to this domain.
The paper tackles the problem of designing digital interfaces that adapt to individual user habits by developing a Bayesian machine learning approach for online incremental learning of browsing behavior, with simulations showing effectiveness in both stationary and non-stationary environments.
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 group 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 navigation 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 interface.