Enterprise Marketing Decision: Advertising Click Through Rate Prediction Based on Deep Neural Networks

Abstract: With the high-speed growth of modern information technology, online advertising, as a new form of advertising on the Internet, has begun to emerge, demonstrating enormous development potential. To improve the accurate estimation of advertising placement and improve the operational efficiency of the advertising placement system, an improved deep neural network model for forecasting advertising click through rate was studied and designed. Meanwhile, the values of the activation function and the parameter dropout are determined, and the prediction accuracy of the deep neural network model and the improved model is compared and analyzed. The experimental results show that the training time of the improved prediction model has been shortened by about 73.25%, resulting in a significant improvement in computational efficiency. When the number of iterations is 110, the logarithmic loss function value is 0.208, and the logarithmic loss function value of the improved model is 0.207, with an average loss reduction of 0.4%. In the area comparison under the receiver operating characteristic curve, the pre improved model was 0.7092, and the improved model was 0.7207. Meanwhile, compared to before the improvement, the prediction accuracy of the improved model increased by 1.6%. The data validates that the optimized model has high prediction precision and efficiency, and has certain application potential and commercial value in marketing.

Luyao Zhan. “Enterprise Marketing Decision: Advertising Click Through Rate Prediction Based on Deep Neural Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.9 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140988

@article{Zhan2023,
title = {Enterprise Marketing Decision: Advertising Click Through Rate Prediction Based on Deep Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140988},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140988},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Luyao Zhan}
}