Learning to Rank Using Gradient Descent

author: Chris Burges, Microsoft Research
published: Dec. 5, 2015,   recorded: October 2015,   views: 237
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Description

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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