An Empirical Investigation of Ceteris Paribus Learnability

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.