Related papers: Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift

Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift

URL: http://arxiv.org/abs/2505.17203v1
Date: Thu, 22 May 2025 18:18:17 GMT
Title: Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift
Authors: Yi Zhang, Elynn Chen, Yujun Yan,
Abstract summary: We study contextual dynamic pricing when a target market can leverage K auxiliary markets.<n>We propose Cross-Market Transfer Dynamic Pricing (CM-TDP), the first algorithm that provably handles such model-shift transfer.<n>By bridging transfer learning, robust aggregation, and revenue optimization, CM-TDP moves toward pricing systems that transfer faster, price smarter.
Score: 5.471147654736597
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We study contextual dynamic pricing when a target market can leverage K auxiliary markets -- offline logs or concurrent streams -- whose mean utilities differ by a structured preference shift. We propose Cross-Market Transfer Dynamic Pricing (CM-TDP), the first algorithm that provably handles such model-shift transfer and delivers minimax-optimal regret for both linear and non-parametric utility models. For linear utilities of dimension d, where the difference between source- and target-task coefficients is $s_{0}$-sparse, CM-TDP attains regret $\tilde{O}((d*K^{-1}+s_{0})\log T)$. For nonlinear demand residing in a reproducing kernel Hilbert space with effective dimension $\alpha$, complexity $\beta$ and task-similarity parameter $H$, the regret becomes $\tilde{O}\!(K^{-2\alpha\beta/(2\alpha\beta+1)}T^{1/(2\alpha\beta+1)} + H^{2/(2\alpha+1)}T^{1/(2\alpha+1)})$, matching information-theoretic lower bounds up to logarithmic factors. The RKHS bound is the first of its kind for transfer pricing and is of independent interest. Extensive simulations show up to 50% lower cumulative regret and 5 times faster learning relative to single-market pricing baselines. By bridging transfer learning, robust aggregation, and revenue optimization, CM-TDP moves toward pricing systems that transfer faster, price smarter.

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