Dynamic temporal reinforcement learning and policy-enhanced LSTM for hotel booking cancellation prediction.

ABSTRACT: The global tourism industry is expanding rapidly, making effective management of hotel booking cancellations crucial for improving service and efficiency. Existing models, based on static data assumptions and fixed parameters, fail to capture dynamic changes and temporal trends. Real-world cancellation decisions are influenced by factors such as seasonal variations, market demand fluctuations, holidays, and special events, which cause significant changes in cancellation rates. Traditional models struggle to adjust dynamically to these changes. This article proposes a novel approach using deep reinforcement learning techniques for predicting hotel booking cancellations over time. We introduce a framework that combines dynamic temporal reinforcement learning with policy-enhanced LSTM, captur

SUBMITTER: Xiao J 

PROVIDER: S-EPMC11784724 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature