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

doi:10.3969/j.issn.1000-5641.201921007

Abstract

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

CLC Number:

TP391

HE Xiaojuan, GUO Xinshun. Research on an advertising click-through rate prediction model based on feature optimization[J]. Journal of East China Normal University(Natural Science), 2020, 2020(4): 147-155.

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