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

HE Xiaojuan; GUO Xinshun

doi:10.3969/j.issn.1000-5641.201921007

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Jul. 2020

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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 Sciences), 2020, (4): 147-155. doi: 10.3969/j.issn.1000-5641.201921007

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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 Sciences), 2020, (4): 147-155. doi: 10.3969/j.issn.1000-5641.201921007

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

doi: 10.3969/j.issn.1000-5641.201921007

HE Xiaojuan,
GUO Xinshun^,

School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai　201620, China

Received Date: 2019-08-01
Available Online: 2020-07-20
Publish Date: 2020-07-20

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

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References(16)

References

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