The complexity of unsupervised learning of lexicographic preferences. (arXiv:2209.11505v1 [cs.AI])

This paper considers the task of learning users' preferences on a
combinatorial set of alternatives, as generally used by online configurators,
for example. In many settings, only a set of selected alternatives during past
interactions is available to the learner. Fargier et al. [2018] propose an
approach to learn, in such a setting, a model of the users' preferences that
ranks previously chosen alternatives as high as possible; and an algorithm to
learn, in this setting, a particular model of …

arxiv complexity unsupervised unsupervised learning