| Title | : | Learning to price: Interpretable Attribute-level demand models for dynamic markets |
| Speaker | : | Srividhya S (IIT Madras) |
| Details | : | Tue, 13 Jan, 2026 11:00 AM @ SSB 233 |
| 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 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. 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. |
