We consider the predictive problem of supervised ranking, where the task is
to rank sets of candidate items returned in response to queries. Although there
exist statistical procedures that come with guarantees of consistency in this
setting, these procedures require that individuals provide a complete ranking
of all items, which is rarely feasible in practice. Instead, individuals
routinely provide partial preference information, such as pairwise comparisons
of items, and more practical approaches to ranking have aimed at modeling this
partial preference data directly.