Dynamic temporal reinforcement learning and policy-enhanced LSTM for hotel booking cancellation prediction
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 cancellat...
| Published in: | PeerJ Computer Science |
|---|---|
| Main Author: | |
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211153139&doi=10.7717%2fpeerj-cs.2442&partnerID=40&md5=299dab8c7b50df1f26e005cba0294c08 |