Model Ensemble for Click Prediction in Bing Search Ads

  • Xiaoliang Ling ,
  • Weiwei Deng ,
  • Chen Gu ,
  • Hucheng Zhou ,
  • Cui Li ,
  • Feng Sun ,
  • Hucheng Zhou


Published by ACM

Accurate estimation of the click-through rate (CTR) in sponsored ads significantly impacts the user search experience and businesses’ revenue, even 0.1% of accuracy improvement would yield greater earnings in the hundreds of millions of dollars. CTR prediction is generally formulated as a supervised classification problem. In this paper, we share our experience and learning on model ensemble design and our innovation. Specifically, we present 8 ensemble methods and evaluate them on our production data. Boosting neural networks with gradient boosting decision trees turns out to be the best. With larger training data, there is a nearly 0.9% AUC improvement in offline testing and significant click yield gains in online traffic. In addition, we share our experience and learning on improving the quality of training.