Query-based learning of acyclic conditional preference networks from contradictory preferences

Fabien Labernia (), Florian Yger (), Brice Mayag () and Jamal Atif ()
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Fabien Labernia: Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE
Florian Yger: Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE
Brice Mayag: Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE
Jamal Atif: Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE

EURO Journal on Decision Processes, 2018, vol. 6, issue 1, No 3, 39-59

Abstract: Abstract Conditional preference networks (CP-nets) provide a compact and intuitive graphical tool to represent the preferences of a user. However, learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose, in this paper, a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In particular, our algorithm takes into account the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. We provide complexity results of the algorithm, and demonstrate its efficiency through an empirical evaluation on synthetic and on real databases.

Keywords: Query-based learning algorithm; Contradictory preferences; Preference learning; Conditional preference networks (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s40070-017-0070-3

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