Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets

TL;DR

This paper introduces an interpretable additive attribute-level model for dynamic pricing, along with an online learning algorithm that efficiently adapts to market changes and provides transparent price explanations.

Contribution

It proposes a novel additive attribute decomposition model and a gradient-free online learning algorithm for scalable, interpretable dynamic pricing.

Findings

Achieves near-optimal pricing in dynamic markets

Rapidly adapts to market shocks and drifts

Provides transparent attribute-level price explanations

Abstract

Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual product attributes influence price. We address this by introducing an interpretable \emph{Additive Feature Decomposition-based Low-Dimensional Demand (\textbf{AFDLD}) model}, where product prices are expressed as the sum of attribute-level contributions and substitution effects are explicitly modeled. Building on this structure, we propose \textbf{ADEPT} (Additive DEcomposition for Pricing with cross-elasticity and Time-adaptive learning)-a projection-free, gradient-free online learning algorithm that operates directly in attribute space and achieves a sublinear regret of $\tilde{\mathcal{O}}(\sqrt{d}T^{3/4})$. Through controlled synthetic studies and real-world datasets, we show that ADEPT (i) learns near-optimal prices under dynamic market conditions, (ii) adapts rapidly to shocks and drifts, and (iii) yields transparent, attribute-level price explanations. The results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly through structured, attribute-driven representations.