Learning to rank using gradient descent

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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.

Christopher J. C. Burges, Tal Shaked, Erin Renshaw

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Gradient Descent Methods | ICML 2005 | Machine Learning | Probabilistic Cost Function | Ranking Function |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2005
Where	ICML
Authors	Christopher J. C. Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Gregory N. Hullender

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Learning to rank using gradient descent

Gradient Descent Methods | ICML 2005 | Machine Learning | Probabilistic Cost Function | Ranking Function |

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