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Feature Engineering of Click-through-rate Prediction for Advertising

  • Jie RenEmail author
  • Jian Zhang
  • Jing Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

We present the problem of click-through-rate (CTR) for search advertising in ALiMaMa, which displays user information, item information, shop information and trade results. Traditionally, people use logistic regression (LR) to predict it. However, because of the lack of learning ability and the sparse feature matrix, the prediction results are always not so satisfying. In this paper, we mainly propose some feature engineering methods based on gradient boosting decision tree (GBDT) and Bayesian smoothing to obtain a wonderful feature, which has more useful information and is not so sparse. Also, we use xgboost (XGB) instead of LR as our prediction model. The proposed methods are evaluated using offline experiments and the experiment results prove that the log loss drop near \(5\%\) after using these feature engineering methods and XGB. Obviously, it is an excellent performance.

Keywords

CTR Feature engineering GBDT Bayesian smoothing XGBoost 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61671138, 61731006), and was partly supported by the 111 Project No. B17008.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina

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