Predicting hotel booking cancellations using tree-based neural network
Year: 2024
Type: article
Abstract: In the hospitality business, cancellations negatively affect the precise estimation of revenue management. With today's powerful computational advances, it is feasible to develop a model to predict cancellations to reduce the risks for business owners. Although these models have not yet been tested in real-world conditions, several prototypes were developed and deployed in two hotels. The their main goal was to study how these models could be incorporated into a decision support system and to assess their influence on demand-management decisions. In our study, we introduce a tree-based neural network (TNN) that combines a tree-based learning algorithm with a feed-forward neural network as a computational method for predicting hotel booking cancellation. Experimental results indicated that the TNN model significantly improved the predictive power on two benchmark datasets compared to tree-based models and baseline artificial neural networks alone. Also, the preliminary success of our study confirmed that tree-based neural networks are promising in dealing with tabular data.
Source: PeerJ Computer Science
Authors: Dan Yang, Xiaoling Miao
Institution: Wuhan Technical University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,395
OpenAlex ID: W4404489507