A Logic of Relative Desire (Preliminary Report)
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Efficient and non-parametric reasoning over user preferences
User Modeling and User-Adapted Interaction
On the Complexity of Learning Lexicographic Strategies
The Journal of Machine Learning Research
Democratic approximation of lexicographic preference models
Proceedings of the 25th international conference on Machine learning
Label ranking by learning pairwise preferences
Artificial Intelligence
Extending CP-nets with stronger conditional preference statements
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Learning conditional preference networks with queries
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning conditional preference networks with queries
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning User Preferences for 2CP-Regression for a Recommender System
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Learning conditional preference networks
Artificial Intelligence
Learning conditionally lexicographic preference relations
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
An empirical investigation of ceteris paribus learnability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We address the problem of learning preference relations on multi-attribute (or combinatorial) domains. We do so by making a very simple hypothesis about the dependence structure between attributes that the preference relation enjoys, namely separability (no preferential dependencies between attributes). Given a set of examples consisting of comparisons between alternatives, we want to output a separable CP-net, consisting of local preferences on each of the attributes, that fits the examples. We consider three forms of compatibility between a CP-net and a set of examples, and for each of them we give useful characterizations as well as complexity results.