Enhancing sparse data performance in e-commerce dynamic pricing with reinforcement learning and pre-trained learning
Markov decision process
price elasticity of demand
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AbstractThis 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 . 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.
CitationLiu Y, Man KL, Li G, Payne T, Yue Y (2023) 'Enhancing sparse data performance in e-commerce dynamic pricing with reinforcement learning and pre-trained learning', 2023 International Conference on Platform Technology and Service (PlatCon) - Busan, Institute of Electrical and Electronics Engineers Inc..
TypeConference papers, meetings and proceedings