Learning and Optimizing with an SSB Representation of Intransitive Preferences on Sets - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Learning and Optimizing with an SSB Representation of Intransitive Preferences on Sets

Résumé

We propose a Skew-Symmetric Bilinear (SSB) model to represent intransitive preferences on subsets of a ground set of items. More precisely, the SSB model accounts for preference intensities between pairs of subsets. We provide a procedure to learn the parameters of the SSB model from a set of known pairwise preferences between subsets, managing to find a sparse model, and as simple as possible in terms of the degree of interaction between items. The SSB model can be viewed as a concise representation of a weighted tournament on subsets. We study the complexity of determining the winners according to various tournament rules. Numerical tests on synthetic and real-world data are carried out.

Dates et versions

hal-04748698 , version 1 (22-10-2024)

Identifiants

Citer

Hugo Gilbert, Mohamed Ouaguenouni, Olivier Spanjaard. Learning and Optimizing with an SSB Representation of Intransitive Preferences on Sets. ECAI - 27th European Conference on Artificial Intelligence, Oct 2024, Santiago de Compostela, Spain. pp.3372 - 3379, ⟨10.3233/FAIA240887⟩. ⟨hal-04748698⟩
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