600 research outputs found
Online Ranking: Discrete Choice, Spearman Correlation and Other Feedback
Given a set of objects, an online ranking system outputs at each time
step a full ranking of the set, observes a feedback of some form and suffers a
loss. We study the setting in which the (adversarial) feedback is an element in
, and the loss is the position (0th, 1st, 2nd...) of the item in the
outputted ranking. More generally, we study a setting in which the feedback is
a subset of at most elements in , and the loss is the sum of the
positions of those elements.
We present an algorithm of expected regret over a time
horizon of steps with respect to the best single ranking in hindsight. This
improves previous algorithms and analyses either by a factor of either
, a factor of or by improving running
time from quadratic to per round. We also prove a matching lower
bound. Our techniques also imply an improved regret bound for online rank
aggregation over the Spearman correlation measure, and to other more complex
ranking loss functions
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