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An Empirical Investigation of Ceteris Paribus Learnability

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1537-1542.

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.

Loizos Michael, and Elena Papageorgiou. 2013. An Empirical Investigation of Ceteris Paribus Learnability. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 1537–1542. https://ijcai.org/proceedings/2013/229
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