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

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 O((d*K^-1+s_0) T). For nonlinear demand residing in a reproducing kernel Hilbert space with effective dimension , complexity and task-similarity parameter H, the regret becomes O\!(K^-2/(2+1)T^1/(2+1) + H^2/(2+1)T^1/(2+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.

Tasks

Reproductions