Work

Thompson sampling for multi-armed bandits in big data environments

Year: 2024

Type: article

Abstract: 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.

Source: Korean Journal of Applied Statistics

DOI: 10.5351/kjas.2024.37.5.663

Authors: Min Kyong Kim, Beom Seuk Hwang

Institution: Chung-Ang University

Language: English

Topic: Advanced Bandit Algorithms Research

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funders: National Research Foundation, Chung-Ang University, National Research Foundation of Korea, Ministry of Science and ICT, South Korea

Awards: G3096211604, G6416034259