.png){kind=icon}
返回
Predicting hotel booking cancelation with machine learning techniques
.png){kind=icon}
PRE
AI
DOI：10.1108/JHTT-07-2022-0227.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Purpose- The purpose of this study is to develop a model that accurately forecasts hotel room cancelations and further determines the key cancelation drivers.Design/methodology/approach- Predictive modeling, specifically the machine learning methods, is used to forecast room cancelations and identify the main cancelation factors.Findings- By using three different classification algorithms, this study demonstrates that hotel room cancelation can be accurately predicted using XGBoost, as well as the ensemble method involving Support Vector Machine, Random Forest and XGBoost.Originality/value- This study attempted to forecast hotel room cancelations by applying a relatively new method, machine learning. By implementing predictive modeling, one of the most emerging and innovative research methods, this study ultimately provides prediction suggestions in various aspects and levels for hotel management operations.
Keyword:
Forecasting
Machine learning
Revenue management
Big data
Predictive modeling
Hotel booking cancelation
期刊
IF:
6.9
论文数:
450
被引数:
2.4K
机构
引用论文
Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making用于酒店业大数据分析的人工智能: 开发餐厅评论对客户决策有用的预测模型
PREAI
分享收藏Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index基于PCA和改进BPNN的百度指数支持下的有效游客量预测.png)
TOURISM MANAGEMENT
TOURISM MANAGEMENTIF12.4
PREAI
分享收藏分享收藏
查看更多内容