An empirical investigation of ceteris paribus learnability

Authors:
Loizos Michael;Elena Papageorgiou
Affiliations:
Open University of Cyprus;Open University of Cyprus
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 6
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Abstract

Eliciting user preferences constitutes a major step towards developing recommender systems and decision support tools. Assuming that preferences are ceteris paribus allows for their concise representation as Conditional Preference Networks (CP-nets). This work presents the first empirical investigation of an algorithm for reliably and efficiently learning CP-nets in a manner that is minimally intrusive. At the same time, it introduces a novel process for efficiently reasoning with (the learned) preferences.