Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine

  • Thore Graepel ,
  • Joaquin Quiñonero Candela ,
  • Thomas Borchert ,
  • Ralf Herbrich

Proceedings of the 27th International Conference on Machine Learning ICML 2010, Invited Applications Track (unreviewed, to appear) |

Invited Applications Track

We describe a new Bayesian click-through rate (CTR) prediction algorithm used for Sponsored Search in Microsoft’s Bing search engine. The algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities. It maintains Gaussian beliefs over weights of the model and performs Gaussian online updates derived from approximate message passing. Scalability of the algorithm is ensured through a principled weight pruning procedure and an approximate parallel implementation. We discuss the challenges arising from evaluating and tuning the predictor as part of the complex system of sponsored search where the predictions made by the algorithm decide about future training sample composition. Finally, we show experimental results from the production system and compare to a calibrated Naïve Bayes algorithm.