Pricing with Contextual Elasticity and Heteroscedastic Valuation
Primary Area: learning theory
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Keywords: dynamic pricing, online learning
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TL;DR: We propose a new demand model by assuming a feature-based practice elasticity, and design an algorithm that achieves the optimal regret.
Abstract: We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the problem, we propose a computationally efficient algorithm called "Pricing with Perturbation (PwP)", which enjoys an $O(\sqrt{dT\log T})$ regret while allowing arbitrary adversarial input context sequences. We also prove a matching lower bound at $\Omega(\sqrt{dT})$ to show the optimality (up to $\log T$ factors). Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.
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Submission Number: 4003
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