Research on CTR prediction model based on deep neural network

Anfeng Zhang; Guanglei Qi

doi:10.1117/12.2641504

10 November 2022 Research on CTR prediction model based on deep neural network

Anfeng Zhang, Guanglei Qi

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480O (2022) https://doi.org/10.1117/12.2641504
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

With the advent of the era of big data, it takes a lot of time and manpower to build a model that can automatically mine the effective features of data and get the user click-through rate prediction model through training, because the single model classifier needs to extract effective features and input them into the model training by means of feature processing and data mining. In view of the shortcomings of website click-through rate prediction technology, this paper proposes a model based on compressed incentive network to extract the influence of a single factor in the overall project characteristics. The paper also applies the neuron mechanism to extract the effective features of the original features, fuses the features proposed by the two models into new features and adds them to the deep neural network for training. Experiments are designed to prove the rationality of the method. The experimental results show that compared with the current commonly used model, it can improve the AUC index and keep the efficiency within a reasonable range. The research results of this paper are not only of great significance to the development of science and technology, but also affect our daily life and economic consumption all the time.

Citation Download Citation

Anfeng Zhang and Guanglei Qi "Research on CTR prediction model based on deep neural network", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480O (10 November 2022); https://doi.org/10.1117/12.2641504

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KEYWORDS

Data modeling

Neural networks

Performance modeling

Feature extraction

Fermium

Frequency modulation

Neurons

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