Impact of GDPR Compliance on Advertising Recommendation Systems: Algorithmic Challenges, Privacy-Preserving Solutions, and Performance Trade Offs

Europe’s General Data Protection Regulation (GDPR) disrupted traditional advertising recommendation architectures based on unrestricted behavioral profiling towards privacy-centric algorithms. This study aims to examine how the GDPR’s restrictions on personal data usage catalyzed algorithmic breakthroughs in ad algorithms — differential privacy implementation, federated learning architectures, and contextual targeting mechanisms. Empirical findings demonstrate that while regulatory compliance initially imposed measurable efficiency costs—including a 2.1% reduction in click-through rate and 5.4% decrease in conversion rate — subsequent technological adaptations proved remarkably resilient. Privacy-preserving methodologies enabled an ecosystem to move towards architectures balancing user data protection with commercial sustainability and accuracy. This research paper aims to establish a foundational framework for technologists, academics, and practitioners addressing the convergence of privacy regulation and recommendation system engineering in current advertising environments.