Reinforcement Learning in Dynamic Pricing Models for E-Commerce
Article Sidebar
Main Article Content
Abstract
Dynamic pricing has revolutionized the e-commerce industry by enabling businesses to adapt prices in real time to maximize revenue and customer satisfaction. This paper explores the application of reinforcement learning (RL) in dynamic pricing models, highlighting how RL can optimize pricing strategies by learning from historical and real-time data. The discussion includes an overview of traditional dynamic pricing methods, the advantages of RL in this context, implementation challenges, and real-world applications. The findings suggest that RL offers significant potential for improving pricing efficiency, enhancing customer experience, and driving competitive advantages in e-commerce.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
C. Yin and J. Han, “Dynamic pricing model of e-commerce platforms based on deep reinforcement learning,” Comput. Model. Eng. Sci., vol. 127, no. 1, pp. 291–307, 2021, doi: https://doi.org/10.32604/cmes.2021.014347.
S. Suresh Kumar, M. Margala, S. Siva Shankar, and P. Chakrabarti, “A novel weight-optimized LSTM for dynamic pricing solutions in e-commerce platforms based on customer buying behaviour,” Soft Comput., pp. 1–13, 2023, doi: https://doi.org/10.1007/s00500-023-08729-1.
A. Agnihotri and I. I. Raj, “Advanced Deep Reinforcement Learning Framework for Dynamic Pricing Optimization in E-commerce Marketplaces,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–6. doi: 10.1109/ICCCNT61001.2024.10725071.
H. Liu, “Reinforcement Learning for E-Commerce Dynamic Pricing,” in International Conference on Economic Management and Green Development, 2023, pp. 2051–2060. doi: https://doi.org/10.1007/978-981-97-0523-8_183.
M. Sarkar et al., “Optimizing e-commerce profits: A comprehensive machine learning framework for dynamic pricing and predicting online purchases,” J. Comput. Sci. Technol. Stud., vol. 5, no. 4, pp. 186–193, 2023, doi: https://doi.org/10.32996/jcsts.2023.5.4.19.
Y. Subbarayudu, G. V. Reddy, M. V. K. Raj, K. Uday, M. D. Fasiuddin, and P. Vishal, “An efficient novel approach to E-commerce retail price optimization through machine learning,” in E3S Web of Conferences, 2023, vol. 391, p. 1104. doi: https://doi.org/10.1051/e3sconf/202339101104.