Research

Ranking tasks are defined through losses that measure trade-offs between different desiderata such as the relevance and the diversity of the items at the top of the list. This paper addresses the question of which of these tasks are asymptotically solved by sorting by decreasing order of expected utility, for some suitable notion of utility, or, equivalently, when is square loss regression consistent for ranking via score-and-sort? We answer to this question by finding a characterization of ranking losses for which a suitable regression is consistent. This characterization has two strong corollaries. First, whenever there exists a consistent approach based on convex risk minimization, there also is a consistent approach based on regression. Second, when regression is not consistent, there are data distributions for which consistent surrogate approaches necessarily have non-trivial local minima, and for which optimal scoring function are necessarily discontinuous, even when the underlying data distribution is regular. In addition to providing a better understanding of surrogate approaches for ranking, these results illustrate the intrinsic difficulty of solving general ranking problems with the score-and-sort approach.