Distribution-Free Contextual Dynamic Pricing
From MaRDI portal
Publication:6149408
DOI10.1287/MOOR.2023.1369arXiv2109.07340OpenAlexW4376149422MaRDI QIDQ6149408
Yufeng Liu, Yiyun Luo, Wei Sun
Publication date: 5 March 2024
Published in: Mathematics of Operations Research (Search for Journal in Brave)
Abstract: Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer's valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer's true valuation, but instead needs to learn the valuation by leveraging contextual information and historical binary purchase feedbacks. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, where a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sub-linear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto-loan dataset.
Full work available at URL: https://arxiv.org/abs/2109.07340
This page was built for publication: Distribution-Free Contextual Dynamic Pricing
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6149408)
