An Empirical Investigation of Ceteris Paribus Learnability
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.
Cite
Text
Michael and Papageorgiou. "An Empirical Investigation of Ceteris Paribus Learnability." International Joint Conference on Artificial Intelligence, 2013.Markdown
[Michael and Papageorgiou. "An Empirical Investigation of Ceteris Paribus Learnability." International Joint Conference on Artificial Intelligence, 2013.](https://mlanthology.org/ijcai/2013/michael2013ijcai-empirical/)BibTeX
@inproceedings{michael2013ijcai-empirical,
title = {{An Empirical Investigation of Ceteris Paribus Learnability}},
author = {Michael, Loizos and Papageorgiou, Elena},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2013},
pages = {1537-1543},
url = {https://mlanthology.org/ijcai/2013/michael2013ijcai-empirical/}
}