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Abstract
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.
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This work is related to Arnauld and SUPPLE projects.Slides
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The code for learning weights from constraints is available upon request. It is written in Java and can be used either as a library or as a web service accessible via XML RPC.Citation
Gajos, K. and Weld, D. S. (2005). 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. ACM Press.