Sequential Block Elimination for Dynamic Pricing

• Published in: IEEE ... International Conference on Data Mining workshops pp. 125 - 129
• Main Authors: Parambath, Shameem A Puthiya, Alfahad, Saleh Abdullah M, Anagnostopoulos, Christos, Kolomvatsos, Kostas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.12.2024
• Subjects: Conferences; Data mining; Dynamic Pricing; Logistics; Multi-Armed Bandits; Object recognition; Pricing; Pure-Exploration; Thompson Sampling
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW65004.2024.00023

In this paper, we propose a Thompson Sampling based sequential block elimination approach for dynamic pricing within pure-exploration Multi-Armed Bandit (MAB) setting. Given an l-dimensional action space, which represents various price ranges, our objective is to identify the optimal prices for different alternatives or variants of a product in order to maximize the total revenue. Our contribution lies in the development of a novel block elimination-based MAB algorithm. The proposed algorithm begins by discretizing the continuous action space into a finite set of discrete actions. Subsequently, a recursive block elimination procedure is employed to progressively remove sub-optimal actions from the set. The elimination process leverages the calculation of confidence bounds over the blocks of actions, enabling the efficient exclusion of sub-optimal choices. We conduct extensive experiments on a dynamic pricing problem in the logistics domain to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that our method is able to identify the optimal arms within the given budget.