Practical Lessons from Predicting Clicks on Ads at Facebook

@inproceedings{He2014PracticalLF,
  title={Practical Lessons from Predicting Clicks on Ads at Facebook},
  author={Xinran He and Junfeng Pan and Ou Jin and Tianbing Xu and Bo Liu and Tao Xu and Yanxin Shi and Antoine Atallah and Ralf Herbrich and Stuart Bowers and Joaquin Qui{\~n}onero Candela},
  booktitle={ADKDD@KDD},
  year={2014}
}
Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by… CONTINUE READING
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