Thompson sampling for multi-armed bandits in big data environments

The multi-armed bandits (MAB) problem, involves selecting actions to maximize rewards within dynamic environments. This study explores the application of Thompson sampling, a robust MAB algorithm, within the context of big data analytics and statistical learning theory. By leveraging large-scale banner click data from recommendation systems, we evaluate Thompson sampling's performance across various simulated scenarios, employing advanced approximation techniques. Our findings demonstrate that Thompson sampling, particularly with Langevin Monte Carlo approximation, maintains robust performance and scalability in big data environments. This underscores its practical significance and adaptability, aligning with contemporary challenges in statistical learning