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.
Title
On the Consistency of Ranking Algorithms
Published
2010-05-09
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2010-56
Type
Text
Extent
12 p
Archive
The Engineering Library
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