Abstract
Like relational probabilistic models, the need for relational preference models naturally arises in real-world applications involving multiple, heterogeneous, and richly interconnected objects. On the one hand, relational preferences should be represented into statements which are natural for human users to express. On the other hand, relational preference models should be endowed with a structure that supports tractable forms of reasoning and learning. This paper introduces the framework of conditional preference relational networks (CPR-nets), that maintains the spirit of the popular “CP-nets” by expressing relational preferences in a natural way using the ceteris paribus semantics. We show that acyclic CPR-nets support tractable inference for optimization and ranking tasks. In addition, we show that in the online learning model, tree-structured CPR-nets are efficiently learnable from both optimization tasks and ranking tasks. Our results are corroborated with experiments on a large-scale movie recommendation dataset.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A tool for representing and reasoning with conditional Ceteris Paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)
Brafman, R.I., Domshlak, C.: Preference handling - an introductory tutorial. AI Magazine 30(1), 58–86 (2009)
Brafman, R.I., Domshlak, C., Shimony, S.E.: On graphical modeling of preference and importance. J. Artif. Intell. Res. 25, 389–424 (2006)
Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In: Proc. of the 5th ACM Conference on Recommender Systems, RecSys 2011 (2011)
Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games, Cambridge (2006)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Tutte, W.T.: Graph Theory, Cambridge (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koriche, F. (2012). Relational Networks of Conditional Preferences. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-31951-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31950-1
Online ISBN: 978-3-642-31951-8
eBook Packages: Computer ScienceComputer Science (R0)
