Decision-theoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck - in most cases the numerous parameters of these functions are chosen manually, which is a tedious and error-prone process. This paper describes ARNAULD, a general interactive tool for eliciting user preferences concerning concrete outcomes and using this feedback to automatically learn a factored cost function. We empirically evaluate our machine learning algorithm and two automatic query generation approaches and report on an informal user study.
Krzysztof Gajos and Daniel S. Weld. Preference elicitation for interface optimization. In UIST '05: Proceedings of the 18th annual ACM symposium on User interface software and technology, pages 173-182, New York, NY, USA, 2005. ACM Press.BibTeX