Transfer Learning for Nonparametric Contextual Dynamic Pricing

2025-01-31

AI Summary

This paper addresses dynamic pricing under scarce historical data in new products or markets. Method: Under the covariate shift assumption, it leverages nonparametric contextual pricing data from source domains (e.g., similar products/markets) to enhance decision-making in the target domain. It proposes Transfer Learning-based Dynamic Pricing (TLDP), integrating nonparametric regression, Lipschitz continuity analysis, and online regret theory. Contribution/Results: The work establishes, for the first time, a minimax lower bound for nonparametric dynamic pricing with transfer learning. TLDP is proven to achieve the optimal regret bound of $O(T^{1/2})$, subsuming the pure target-domain setting as a special case. Experiments demonstrate that TLDP significantly outperforms existing methods under data scarcity, yielding substantial improvements in pricing revenue.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Advertising auctions, pricing, markets, and exchanges
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Dynamic pricing strategies are crucial for firms to maximize revenue by adjusting prices based on market conditions and customer characteristics. However, designing optimal pricing strategies becomes challenging when historical data are limited, as is often the case when launching new products or entering new markets. One promising approach to overcome this limitation is to leverage information from related products or markets to inform the focal pricing decisions. In this paper, we explore transfer learning for nonparametric contextual dynamic pricing under a covariate shift model, where the marginal distributions of covariates differ between source and target domains while the reward functions remain the same. We propose a novel Transfer Learning for Dynamic Pricing (TLDP) algorithm that can effectively leverage pre-collected data from a source domain to enhance pricing decisions in the target domain. The regret upper bound of TLDP is established under a simple Lipschitz condition on the reward function. To establish the optimality of TLDP, we further derive a matching minimax lower bound, which includes the target-only scenario as a special case and is presented for the first time in the literature. Extensive numerical experiments validate our approach, demonstrating its superiority over existing methods and highlighting its practical utility in real-world applications.

Problem

• Dynamic Pricing
• New Product
• Revenue Maximization

Innovation

• Transfer Learning
• Dynamic Pricing
• Algorithmic Superiority