Learning to Rank using Gradient Descent
- Chris J.C. Burges ,
- Tal Shaked ,
- Erin Renshaw ,
- Ari Lazier ,
- Matt Deeds ,
- Nicole Hamilton ,
- Greg Hullender
MSR-TR-2005-06 |
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.