ML Anthology

Learning to Rank Using Gradient Descent

Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender
ICML 2005 pp. 89-96
doi:10.1145/1102351.1102363 /icml/2005/burges2005icml-learning/

Abstract

We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine.

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Text

Burges et al. "Learning to Rank Using Gradient Descent." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102363

Markdown

[Burges et al. "Learning to Rank Using Gradient Descent." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/burges2005icml-learning/) doi:10.1145/1102351.1102363

BibTeX

@inproceedings{burges2005icml-learning,
  title     = {{Learning to Rank Using Gradient Descent}},
  author    = {Burges, Christopher J. C. and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Gregory N.},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {89-96},
  doi       = {10.1145/1102351.1102363},
  url       = {https://mlanthology.org/icml/2005/burges2005icml-learning/}
}