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

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.