Learning User Interface Preferences via Contextual Discrete Choice Experimentation

Designing effective user interfaces (UIs) is a complex decisionmaking process that often relies on usability testing and understanding users' preferences.However, user preferences can vary widely based on contextual information (such as age, nationality, or use-case of the software system), posing a significant challenge in creating universally effective studies.To address this, we propose a novel framework of contextual discrete choice experimentation (DCE) to learn the relationship between contextual information and user preferences, enabling the creation of more statistically efficient studies for new cohorts of participants.In this framework, users are presented with a sequence of questions where they choose their preferred option between two or more design alternatives.This preference data, combined with contextual information, is used to develop a statistical model that recommends UI designs to new or existing users.We detail the methodology for designing contextual DCEs and demonstrate its application with a real-world example involving users of a statistical software system.Our results indicate that the contextual DCE framework effectively captures user preferences and provides personalized UI recommendations.