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

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 Additive Feature Decomposition-based Low-Dimensional Demand (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 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 sublinear regret of Õ(√d, T3/4). 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.