Learning to rank using gradient descent

  • Authors:

  • Chris Burges;Tal Shaked;Erin Renshaw;Ari Lazier;Matt Deeds;Nicole Hamilton;Greg Hullender

  • Affiliations:

  • Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft, One Microsoft Way, Redmond, WA;Microsoft, One Microsoft Way, Redmond, WA;Microsoft, One Microsoft Way, Redmond, WA;Microsoft, One Microsoft Way, Redmond, WA

  • Venue:
  • ICML '05 Proceedings of the 22nd international conference on Machine learning
  • Year:
  • 2005

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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.