On the Consistency of Ranking Algorithms

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On the Consistency of Ranking Algorithms
We present a theoretical analysis of supervised ranking, providing necessary and sufficient conditions for the asymptotic consistency of algorithms based on minimizing a surrogate loss function. We show that many commonly used surrogate losses are inconsistent; surprisingly, we show inconsistency even in low-noise settings. We present a new value-regularized linear loss, establish its consistency under reasonable assumptions on noise, and show that it outperforms conventional ranking losses in a collaborative filtering experiment. The goal in ranking is to order a set of inputs in accordance with the preferences of an individual or a population. In this paper we consider a general formulation of the supervised ranking problem in which each training example consists of a query q, a set of inputs x, sometimes called results, and a weighted graph G representing preferences over the results. The learning task is to discover a function that provides a queryspecific ordering of the inputs t...
John Duchi, Lester W. Mackey, Michael I. Jordan
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors John Duchi, Lester W. Mackey, Michael I. Jordan
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