Modeling user preferences via theory refinement

Authors:
Ben Geisler;Vu Ha;Peter Haddawy
Affiliations:
Decision Systems and Artificial Intelligence Lab, Dept. of EE&CS, University of Wisconsin-Milwaukee;Decision Systems and Artificial Intelligence Lab, Dept. of EE&CS, University of Wisconsin-Milwaukee;CSIM Program ,School of Advanced Technologies, Asian Institute of Technology, Bangkok, Thailand
Venue:
Proceedings of the 6th international conference on Intelligent user interfaces
Year:
2001

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Abstract

We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We show how to encode assumptions concerning preferential independence and monotonicity in a Knowledge-Based Artificial Neural Network. We quantify the degree to which user preferences violate a set of assumptions. We empirically compare the KBANN network with an unbiased ANN in terms of learning rate and accuracy for preferences consistent and inconsistent with the assumptions. We go on to demonstrate how the technique can be used to learn a fine-grained preference structure from simple binary classification data.