On the Consistency of Ranking Algorithms
Abstract
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.
Cite
Text
Duchi et al. "On the Consistency of Ranking Algorithms." International Conference on Machine Learning, 2010.Markdown
[Duchi et al. "On the Consistency of Ranking Algorithms." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/duchi2010icml-consistency/)BibTeX
@inproceedings{duchi2010icml-consistency,
title = {{On the Consistency of Ranking Algorithms}},
author = {Duchi, John C. and Mackey, Lester W. and Jordan, Michael I.},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {327-334},
url = {https://mlanthology.org/icml/2010/duchi2010icml-consistency/}
}