Online Ranking: Discrete Choice, Spearman Correlation and Other Feedback

TL;DR

This paper introduces an online ranking algorithm with improved regret bounds for adversarial feedback scenarios, optimizing performance for various ranking loss functions including Spearman correlation.

Contribution

The authors develop a new algorithm with tighter regret bounds and faster running time, also providing matching lower bounds and extending results to complex ranking losses.

Findings

Expected regret of $O(n^{3/2}\sqrt{Tk})$ over $T$ steps.

Improved running time from quadratic to $O(n\log n)$ per round.

Extension of bounds to Spearman correlation and other ranking losses.

Abstract

Given a set $V$ of $n$ 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 $V$, 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 $U$ of at most $k$ elements in $V$, and the loss is the sum of the positions of those elements. We present an algorithm of expected regret $O(n^{3/2}\sqrt{Tk})$ over a time horizon of $T$ steps with respect to the best single ranking in hindsight. This improves previous algorithms and analyses either by a factor of either $\Omega(\sqrt{k})$, a factor of $\Omega(\sqrt{\log n})$ or by improving running time from quadratic to $O(n\log n)$ 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.