A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network


Zilong Jiang, Shu Gao, Wei Dai, Journal of Information Processing Systems Vol. 13, No. 5, pp. 1052-1070, Oct. 2017  

https://doi.org/10.3745/JIPS.02.0069
Keywords: deep neural network, Machine Learning, Text Advertising, SAE-LR
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Abstract

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.


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Cite this article
[APA Style]
Jiang, Z., Gao, S., & Dai, W. (2017). A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network . Journal of Information Processing Systems, 13(5), 1052-1070. DOI: 10.3745/JIPS.02.0069.

[IEEE Style]
Z. Jiang, S. Gao, W. Dai, "A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network ," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1052-1070, 2017. DOI: 10.3745/JIPS.02.0069.

[ACM Style]
Zilong Jiang, Shu Gao, and Wei Dai. 2017. A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network . Journal of Information Processing Systems, 13, 5, (2017), 1052-1070. DOI: 10.3745/JIPS.02.0069.