Advanced Journal of Management, Humanity and Social Science

Analysis and Optimization of Ranking Patterns in Advertising Platforms Using Explainable Artificial Intelligence (XAI) and SEO Optimization

Articles in Press

Document Type : Original Article

Authors

Ali Salehi Vojdeh Nazari 1
Mahdi Baradaran 2

1 Master's degree student in Information Technology Management, Electronic Business major, North Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Information Technology Management, North Tehran Branch, Islamic Azad University, Tehran, Iran

zenodo.org/ajmhss.2026.588528.1087
Abstract
The aim of this study is to design and evaluate a hybrid model based on SEO metrics and explainable machine learning algorithms (XAI) to improve the ranking of digital advertisements. The research data consisted of 5,000 simulated records, which after preprocessing were analyzed using XGBoost, Random Forest, and Elastic Net models. Performance evaluation using metrics such as Accuracy, MAE, and RMSE indicated that the XGBoost model outperformed the others. To understand the model's decision-making logic, SHAP and LIME were employed. The results highlight the significant role of content quality, keyword relevance, and click-through rate in determining ad ranking. Additionally, fidelity and stability metrics of explanations showed that the best-performing model not only offers appropriate accuracy but also exhibits high transparency and stability in providing explanations. In the structural analysis, the mediating role of "user trust" in strengthening the effect of SEO metrics was confirmed. Overall, the findings suggest that combining SEO metrics with explainable models can lead to both improved prediction accuracy and enhanced transparency and trustworthiness in ad ranking.

Keywords

Ad Ranking in Advertising Platforms
Explainable Artificial Intelligence
SEO Optimization
Subjects
Advanced Journal of Management, Humanity and Social Science

Articles in Press, Accepted Manuscript
Available Online from 25 June 2026

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