Contextual Dynamic Pricing

Contextual dynamic pricing optimizes pricing strategies by leveraging contextual information about products and customers to maximize revenue. Current research focuses on developing algorithms that achieve minimax optimality and improved regret bounds under various assumptions about customer valuation models, often employing techniques like upper confidence bounds, layered data partitioning, and deep learning architectures (e.g., neural networks and transformers). These advancements address challenges like handling large-scale markets, incorporating fairness constraints, and accounting for strategic buyer behavior, leading to more efficient and ethically sound pricing mechanisms with practical applications in diverse sectors.