Yang Dan, Miao Xiaoling
Wuhan Polytechnic, Wuhan City, Hubei Province, China.
PeerJ Comput Sci. 2024 Nov 18;10:e2473. doi: 10.7717/peerj-cs.2473. eCollection 2024.
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.
在酒店业中，取消预订会对收益管理的精确估计产生负面影响。随着当今强大的计算技术进步，开发一个预测取消预订的模型以降低企业主的风险是可行的。尽管这些模型尚未在实际条件下进行测试，但已经开发了几个原型并在两家酒店中进行了部署。其主要目标是研究如何将这些模型纳入决策支持系统，并评估它们对需求管理决策的影响。在我们的研究中，我们引入了一种基于树的神经网络（TNN），它将基于树的学习算法与前馈神经网络相结合，作为预测酒店预订取消的计算方法。实验结果表明，与仅基于树的模型和基线人工神经网络相比，TNN模型在两个基准数据集上显著提高了预测能力。此外，我们研究的初步成功证实了基于树的神经网络在处理表格数据方面很有前景。
