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Enhancing sparse data performance in e-commerce dynamic pricing with reinforcement learning and pre-trained learning

  • Yuchen Liu
    ,
  • Ka Lok Man
    ,
  • Gangmin Li
    ,
  • Terry R. Payne
    ,
  • Yong Yue
  • Xi'an Jiaotong-Liverpool University
    ,
  • University of Liverpool
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 39-42 (4 pages)

Publication milestones

  • Published - 25/09/2023

Publication status

Published - 25/09/2023

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: 2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings

ISBN (Print)

9798350305999

ISBN (Electronic)

9798350305999

External Publication IDs

  • handle.net: 10547/626154
  • Scopus: 85175401537

Host publication title

2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings

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