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Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction by Shuixia Chen; Eric W.T. Ngai; Yaoyao Ku; Zeshui Xu; Xunjie Gou; Chenxi Zhang is a Computer Science article available to read on EtoBox.

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Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel management. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the probability modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso re

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Author

Shuixia Chen; Eric W.T. Ngai; Yaoyao Ku; Zeshui Xu; Xunjie Gou; Chenxi Zhang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Social Sciences)