Adaptive Parameter Optimization in Large-Scale E-commerce Search Systems: A Reinforcement Learning Approach
Nitish Ratan Appanasamy · International Journal of Advances in Engineering and Management · 2025
Adaptive parameter optimization in large-scale e-commerce search systems through reinforcement learning represents a significant advancement in digital retail platform performance. The three-tiered framework encompasses baseline parameter establishment, predictive parameter adjustment, and reinforcement learning optimization to address the complex challenges of modern e-commerce search. This integrated solution manages massive product catalogs while maintaining precise search relevance and subsecond response times. Through evolutionary optimization techniques and sophisticated machine learning algorithms, the system demonstrates substantial improvements in search quality, resource utilization, and user engagement metrics. The implementation incorporates comprehensive safety mechanisms and robust architectural design principles to ensure system stability under varying load conditions. Results indicate marked enhancements in query processing efficiency, search relevance scores, and computational resource management across diverse product categories. The findings establish the effectiveness of dynamic parameter optimization in handling the multifaceted demands of contemporary ecommerce search environments while maintaining high performance and reliability standards.