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 attributelevel 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 ˜ 𝑂( √ 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 autonomouspricingagentscanbeachievedjointlythroughstructured, attribute-driven representations.

Authors

• Srividhya Sethuraman IIT Madras & TCS Research
• Chandrashekar Lakshminarayanan IIT Madras

Keywords

• Dynamic pricing
• Low-dimensional demand
• Additive Feature Decomposition (AFD)
• Dynamic Markets
• Interpretability
• Attributelevel prices
• LEARN