@inproceedings{e087ef11c97443b78ad057ae94b708cf,
title = "Enhancing sparse data performance in e-commerce dynamic pricing with reinforcement learning and pre-trained learning",
abstract = "This paper introduces a reinforcement learning-based framework designed to tackle dynamic pricing challenges in e-commerce. Prior research has predominantly concentrated on algorithm selection to enhance performance in dense data scenarios. However, many of these models fail to robustly address sparse data structures, such as low-traffic products, leading to the 'cold-start' problem [4]. Through numerical analysis, our framework offers innovative insights derived from the design of the reward function and integrates product clustering with pre-trained learning to mitigate this issue. As a result of this optimization, the performance of predictive models on sparse data is expected to see substantial improvement.",
keywords = "Clustering, Dynamic Pricing, K-means, Markov decision process, Price elasticity of demand, Reinforcement Learning, Sarsa",
author = "Yuchen Liu and Man, \{Ka Lok\} and Gangmin Li and Payne, \{Terry R.\} and Yong Yue",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Platform Technology and Service (PlatCon) ; Conference date: 25-09-2023",
year = "2023",
month = sep,
day = "25",
doi = "10.1109/PlatCon60102.2023.10255211",
language = "English",
isbn = "9798350305999",
series = "2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "39--42",
booktitle = "2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings",
address = "United States",
}