Abstract
Additive preference representation is standard in Multiple Criteria Decision Analysis, and learning such a preference model dates back from the UTA method [11]. In this seminal work, an additive piece-wise linear model is inferred from a learning set composed of pairwise comparisons. In this setting, the learning set is provided by a single Decision-Maker (DM), and an additive model is inferred to match the learning set. We extend this framework to the case where (i) multiple DMs with heterogeneous preferences provide part of the learning set, and (ii) the learning set is provided as a whole without knowing which DM expressed each pairwise comparison. Hence, the problem amounts to inferring a preference model for each DM and simultaneously “discovering” the segmentation of the learning set. In this paper, we show that this problem is computationally difficult. We propose a mathematical programming based resolution approach to solve this Preference Learning and Segmentation problem (PLS). We also propose a heuristic to deal with large datasets. We study the performance of both algorithms through experiments using synthetic and real data.
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Notes
- 1.
Appendices available at github.com/artefactory/learning-heterogeneous-preferences.
- 2.
Appendices available at github.com/artefactory/learning-heterogeneous-preferences.
- 3.
Dataset available at github.com/artefactory/choice-learn.
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Auriau, V., Belahcène, K., Malherbe, E., Mousseau, V. (2025). Learning Multiple Multicriteria Additive Models from Heterogeneous Preferences. In: Freeman, R., Mattei, N. (eds) Algorithmic Decision Theory. ADT 2024. Lecture Notes in Computer Science(), vol 15248. Springer, Cham. https://doi.org/10.1007/978-3-031-73903-3_14
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