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Optimizing Personalized Advertising in Decentralized Ecosystems: A Blockchain and Random Forest-Based Approach

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  • Birzoim, Ammoon

Abstract

This study investigates the integration of Random Forest algorithms and blockchain technology in the domain of decentralized personalized advertising. Through multiple case studies, the report demonstrates applications in fraud detection, transparency enhancement, tokenized loyalty programs, and ad impact measurement. The Random Forest algorithm supports high-dimensional feature selection, classification accuracy, and robustness against data irregularities, while blockchain ensures immutability, auditability, and transactional security. Challenges in federated learning, including non-IID data distributions, heterogeneous device constraints, and communication overhead, are identified. Technical discussions address algorithm convergence, model aggregation frequency, and trust enforcement mechanisms. Future directions include optimization of algorithm performance in decentralized settings, secure model update protocols, and cross-industry adaptation of Blockchain-Federated Learning systems for scalable advertising solutions.

Suggested Citation

  • Birzoim, Ammoon, 2025. "Optimizing Personalized Advertising in Decentralized Ecosystems: A Blockchain and Random Forest-Based Approach," OSF Preprints rvby3_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:rvby3_v1
    DOI: 10.31219/osf.io/rvby3_v1
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    References listed on IDEAS

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    1. Kim, Jooyoung & Lee, Kyu Hyung & Kim, Jaemin, 2023. "Linking blockchain technology and digital advertising: How blockchain technology can enhance digital advertising to be more effective, efficient, and trustworthy," Journal of Business Research, Elsevier, vol. 160(C).
    2. Jin, Keyan & Zhong, Ziqi & Zhao, Elena Yifei, 2024. "Sustainable digital marketing under big data: an AI random forest model approach," LSE Research Online Documents on Economics 121402, London School of Economics and Political Science, LSE Library.
    3. Pearson, Andrew, 2019. "Personalisation the artificial intelligence way," Journal of Digital & Social Media Marketing, Henry Stewart Publications, vol. 7(3), pages 245-269, December.
    4. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LLC, vol. 20(1), pages 3-29, March.
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