Enhancing Sparse Data Performance in E-Commerce Dynamic Pricing with Reinforcement Learning and Pre-Trained Learning

Yuchen Liu, Ka Lok Man, Gangmin Li, Terry Payne, Yong Yue

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages39-42
Number of pages4
ISBN (Electronic)9798350305999
DOIs
Publication statusPublished - 2023
Event9th International Conference on Platform Technology and Service, PlatCon 2023 - Busan, Korea, Republic of
Duration: 16 Aug 202318 Aug 2023

Publication series

Name2023 International Conference on Platform Technology and Service, PlatCon 2023 - Proceedings

Conference

Conference9th International Conference on Platform Technology and Service, PlatCon 2023
Country/TerritoryKorea, Republic of
CityBusan
Period16/08/2318/08/23

Keywords

  • Clustering
  • Dynamic Pricing
  • K-means
  • Markov decision process
  • Price elasticity of demand
  • Reinforcement Learning
  • Sarsa

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