2016
Online Learning to Rank with Feedback at the Top
AISTATS 2016poster
We consider an online learning to rank setting in which, at each round, an oblivious adversary generates a list of m documents, pertaining to a query, and the learner ranks the documents according to assigned scores. The adversary then generates a relevance vector and the learner updates its ranker…