Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (4): 147-155.doi: 10.3969/j.issn.1000-5641.201921007

• Computer Science • Previous Articles     Next Articles

Research on an advertising click-through rate prediction model based on feature optimization

HE Xiaojuan, GUO Xinshun   

  1. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China
  • Received:2019-08-01 Published:2020-07-20

Abstract: This paper proposes an online advertising feature extraction model of CNN (Convolutional Neural Networks) based on GBDT (Gradient Boosting Decision Tree) aimed at solving challenges with high-dimensional sparseness in Internet advertising data based on existing theories and technologies for click-through rate (CRT) prediction. The proposed model, CNN+, is able to extract deep, high-order features from raw data and solve the issues that convolutional neural networks face in extracting sparse and high-dimensional features. Experimental results on real datasets show that the features extracted by the CNN+ model are more effective than two other feature extraction methods studied, namely principal component analysis (PCA) and GBDT.

Key words: advertising click-through rate prediction, gradient boosting decision tree (GBDT), convolutional neural networks (CNN), feature learning

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