4 UT3 - Université Toulouse III - Paul Sabatier (118 route de Narbonne - 31062 Toulouse - France)
- UT - Université de Toulouse (41 Allée Jules Guesde, 31000 Toulouse - France)
Communication Dans Un Congrès
Année : 2024
4 UT3 - Université Toulouse III - Paul Sabatier (118 route de Narbonne - 31062 Toulouse - France)
Résumé
CP-nets are a very expressive graphical model for representing preferences over combinatorial spaces. They are particularly well suited for settings where an important task is to compute the optimal completion of some partially specified alternative; this is, for instance, the case of interactive configurators, where preferences can be used at every step of the interaction to guide the decision maker towards a satisfactory configuration. Learning CP-nets is challenging when the input data has the form of pairwise comparisons between alternatives. Furthermore, this type of preference data is not commonly stored: it can be elicited but this puts an additional burden on the decision maker.
In this article, we propose a new method for learning CP-nets from sales history, a kind of data readily available in many e-commerce applications. The approach is based on the minimum description length (MDL) principle. We show some theoretical properties of this learning task, namely its sample complexity and its NP-completeness, and we experiment with this learning algorithm in a recommendation setting with real sales history from a car maker.
Domaines
Intelligence artificielle [cs.AI]
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learn-CPnets-with-MDL_2024-05-10-12h05-avec-bbl+annexe.pdf (460.06 Ko)
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https://ut3-toulouseinp.hal.science/hal-04572196
Soumis le : lundi 28 octobre 2024-11:39:53
Dernière modification le : jeudi 12 décembre 2024-22:06:25
Dates et versions
Licence
- HAL Id : hal-04572196 , version 1
- DOI : 10.24963/ijcai.2024/376
Citer
Pierre-François Gimenez, Jérôme Mengin. Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle. IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence, Aug 2024, Jeju, South Korea. pp.3395-3403, ⟨10.24963/ijcai.2024/376⟩. ⟨hal-04572196⟩
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