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Predicting hotel booking cancellations using tree-based neural network.

Dan Yang¹, Xiaoling Miao¹

¹Wuhan Polytechnic, Wuhan City, Hubei Province, China.

Peerj. Computer Science

|December 9, 2024

Summary

This summary is machine-generated.

Predicting hotel booking cancellations is crucial for revenue management. A new tree-based neural network (TNN) model significantly improves prediction accuracy, offering promising solutions for the hospitality industry.

Keywords:

Booking prediction Data science Hotel Machine learning Tree-based neural network

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Hotel cancellations disrupt revenue management accuracy.
Computational advancements enable predictive modeling for risk reduction.
Existing models require real-world testing and integration into decision support systems.

Purpose of the Study:

To develop and evaluate a novel computational method for predicting hotel booking cancellations.
To assess the integration of predictive models into hotel decision support systems.
To analyze the impact of predictive models on demand-management strategies.

Main Methods:

Introduction of a tree-based neural network (TNN).
The TNN combines tree-based learning algorithms with feed-forward neural networks.
Model testing on two benchmark datasets.

Main Results:

The TNN model demonstrated significantly improved predictive power.
Performance was superior compared to traditional tree-based models.
The TNN outperformed baseline artificial neural networks.

Conclusions:

Tree-based neural networks show promise for predicting hotel booking cancellations.
The TNN model offers a viable computational approach for tabular data.
Further validation in real-world conditions is suggested.