Learning to rank using gradient descent on ShortScience.org

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Learning to rank using gradient descent
Burges, Christopher J. C. and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Gregory N.
International Conference on Machine Learning - 2005 via Local Bibsonomy
Keywords: dblp

Summaries/Notes 1

[link] Summary by Martin Thoma 5 years ago

[Learning to rank using gradient descent](https://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf) is a paper published in 2005 by Burges et all from Microsoft. The paper introduced RankNet.

RankNet is a neural network for recommendations.

The main use-case of the paper is ranking search results.

## Key Ideas

* Preprocessing: Filter results which are relevant
* Ranking: Rank results which are relevant by RankNet

## See also

* [Adapting deep RankNet for personalized search](https://www.shortscience.org/paper?bibtexKey=conf/wsdm/SongWH14)

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