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Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network

In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional...

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Detalles Bibliográficos
Autor principal: Zhu, Danqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378957/
https://www.ncbi.nlm.nih.gov/pubmed/34422030
http://dx.doi.org/10.1155/2021/3484104
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author Zhu, Danqing
author_facet Zhu, Danqing
author_sort Zhu, Danqing
collection PubMed
description In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy.
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spelling pubmed-83789572021-08-21 Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network Zhu, Danqing Comput Intell Neurosci Research Article In the era of big data information, how to effectively predict and analyze the click-through rate of information advertising is the key for enterprises in various fields to seek returns. The point rate prediction of advertising is one of the core contents of advertising calculation. The traditional shallow prediction model cannot meet the nonlinear relationship of data processing, and the manual processing of data information extraction method is very resource consuming. To solve the above problems, this paper proposes a CNN-LSTM (convolutional neural network-long short-term memory) convolution hybrid neural network algorithm to predict the click-through rate of advertisements. According to the neural network algorithm, the prediction model is constructed, and the effective features are extracted in the process of model establishment, and the prediction analysis is carried out according to the simplified LSTM neural network time serialization features. CNN convolution neural network is used to train the prediction model. This paper analyzes the characteristics of traditional prediction methods and the corresponding solutions and carries out feature learning and prediction model construction for advertising click-through rate prediction. Then, the unknown behavior of advertising users is judged and predicted. The results show that, compared with the single structure network of traditional prediction model, the prediction effect based on CNN-LSTM neural network algorithm has higher accuracy. Hindawi 2021-08-13 /pmc/articles/PMC8378957/ /pubmed/34422030 http://dx.doi.org/10.1155/2021/3484104 Text en Copyright © 2021 Danqing Zhu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhu, Danqing
Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title_full Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title_fullStr Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title_full_unstemmed Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title_short Advertising Click-Through Rate Prediction Based on CNN-LSTM Neural Network
title_sort advertising click-through rate prediction based on cnn-lstm neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378957/
https://www.ncbi.nlm.nih.gov/pubmed/34422030
http://dx.doi.org/10.1155/2021/3484104
work_keys_str_mv AT zhudanqing advertisingclickthroughratepredictionbasedoncnnlstmneuralnetwork