Research on App Advertising Click Rate Evaluation Based on Machine Learning Hybrid Model

Huaicang Li, Guochang Ma, Guodong Ma

2025

Abstract

The role of click-through rate in App advertising is very important, but there are problems such as inaccurate targeting of advertising content and low click-through rate. Traditional data analysis cannot solve the problem of insufficient click-through rate and viewership in App ads, and the evaluation is unreasonable. Therefore, this paper proposes a hybrid model of machine learning for advertising click-through rate prediction analysis. Firstly, the social learning theory is used to evaluate the advertising content, and the indicators are divided according to the advertising rating requirements to reduce the advertising rating Disturbing factors in . Then, social learning theory evaluates the prediction and evaluation of the click-through rate of App advertising, forms an evaluation scheme for the click-through rate of App advertising, and comprehensively analyzes the click-through rate results. MATLAB simulation shows that under certain evaluation criteria, the machine learning hybrid model is better than traditional data analysis in predicting the click-through rate and viewership of App ads.

Paper Citation

in Harvard Style

Li H., Ma G. and Ma G. (2025). Research on App Advertising Click Rate Evaluation Based on Machine Learning Hybrid Model. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 435-440. DOI: 10.5220/0013545100004664

in Bibtex Style

@conference{incoft25,
author={Huaicang Li and Guochang Ma and Guodong Ma},
title={Research on App Advertising Click Rate Evaluation Based on Machine Learning Hybrid Model},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={435-440},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013545100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Research on App Advertising Click Rate Evaluation Based on Machine Learning Hybrid Model
SN - 978-989-758-763-4
AU - Li H.
AU - Ma G.
AU - Ma G.
PY - 2025
SP - 435
EP - 440
DO - 10.5220/0013545100004664
PB - SciTePress