Metric Learning from Limited Pairwise Preference Comparisons

arXiv:2403.19629v1 Announce Type: new
Abstract: We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These items are embedded into $\mathbb{R}^d$ equipped with an unknown Mahalanobis distance shared across users. While recent work shows that it is possible to simultaneously recover the metric and ideal items given $\mathcal{O}(d)$ pairwise comparisons per user, in practice we often have a limited budget …

abstract arxiv cs.lg embedded stat.ml study type work