标题： 非参数上下文动态定价的迁移学习 显示英文标题

标题： Transfer Learning for Nonparametric Contextual Dynamic Pricing

摘要： 动态定价策略对于企业通过根据市场条件和客户特征调整价格来最大化收入至关重要。然而，当历史数据有限时，设计最优的定价策略变得具有挑战性，这在推出新产品或进入新市场时常会发生。 一种有希望克服这一限制的方法是利用来自相关产品或市场的信息来指导关键的定价决策。 在本文中，我们在协变量偏移模型下探讨了非参数上下文动态定价的迁移学习，其中源域和目标域之间的协变量边缘分布不同，但回报函数保持相同。 我们提出了一个新颖的动态定价迁移学习（TLDP）算法，该算法能够有效地利用从源域预先收集的数据来增强目标域中的定价决策。在奖励函数的简单Lipschitz条件下建立了TLDP的后悔上界。 为了证明TLDP的最优性，我们进一步推导了一个匹配的minimax下界，其中包括目标唯一场景作为特例，并首次在文献中呈现。 广泛的数值实验验证了我们的方法，展示了它相对于现有方法的优势，并突出了其在现实世界应用中的实用性。 显示英文摘要

摘要： 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.