Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

In three linesHITL-GB framework for short-term rental dynamic pricing: a contextual bandit algorithm generates price recommendations that a human can accept, modify, or reject. Authors show historical data is structurally equivalent to on-policy warm-up, reducing cold-start from ~150 to ~30 episodes. Validated on 1,461 real nights (April 2022–2026).

Summary generated by Claude — human-verified