Co-regularized Monotone Retargeting for Semi-supervised LeTOR
Shalmali D. Joshi, Rajiv Khanna, Joydeep Ghosh · Society for Industrial and Applied Mathematics eBooks · 2018
This work proposes a new model for listwise Learning to Rank (LeTOR) in an inductive semi–supervised setting. We pose the task as that of ranking in a multiview setting, encountered quite commonly in practice. We formulate a novel and efficient co-regularization mechanism that efficiently enforces agreement between views on the rank order of unlabeled samples. This formulation is based on leveraging the convex structures of isotonic vectors and to the best of our knowledge, the first of such co-regularization based frameworks for semi-supervised ranking. We demonstrate the utility of the method when labels are scarce even in settings where supervision is available only as pairwise preferences as well as in comparison to transductive semi–supervised baselines.