Metric Learning to Rank
Abstract
We study metric learning as problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG. We demonstrate experimental results on standard classification data sets, and a large-scale online dating recommendation problem.
Cite
Text
McFee and Lanckriet. "Metric Learning to Rank." International Conference on Machine Learning, 2010.Markdown
[McFee and Lanckriet. "Metric Learning to Rank." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/mcfee2010icml-metric/)BibTeX
@inproceedings{mcfee2010icml-metric,
title = {{Metric Learning to Rank}},
author = {McFee, Brian and Lanckriet, Gert R. G.},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {775-782},
url = {https://mlanthology.org/icml/2010/mcfee2010icml-metric/}
}