---
title: "An Empirical Investigation of Ceteris Paribus Learnability"
authors:
  - "Loizos Michael"
  - "Elena Papageorgiou"
doi: ""
url: "https://ijcai.org/proceedings/2013/229"
pdf: "https://ijcai.org/proceedings/2013/0229.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1537-1542"
track: ""
keywords:
---

# An Empirical Investigation of Ceteris Paribus Learnability

Loizos Michael, Elena Papageorgiou

*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.