Metric Learning from Limited Pairwise Preference Comparisons
This addresses a practical limitation in metric learning for recommendation systems or user modeling, where data is scarce, though it is incremental as it builds on prior work on preference comparisons.
The paper tackles the problem of learning a Mahalanobis distance metric from limited pairwise preference comparisons per user, showing that with fewer than O(d) comparisons, no information about the metric can be recovered in general, but it becomes possible when items have low-dimensional structure, enabling joint identification with theoretical guarantees and empirical validation.
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 of $o(d)$ comparisons. We study whether the metric can still be recovered, even though it is known that learning individual ideal items is now no longer possible. We show that in general, $o(d)$ comparisons reveal no information about the metric, even with infinitely many users. However, when comparisons are made over items that exhibit low-dimensional structure, each user can contribute to learning the metric restricted to a low-dimensional subspace so that the metric can be jointly identified. We present a divide-and-conquer approach that achieves this, and provide theoretical recovery guarantees and empirical validation.