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Antonio, N., de Almeida, A. & Nunes, L. (2017). Predicting hotel bookings cancellation with a machine learning classification model. In 16th IEEE International Conference on Machine Learning and Applications (ICMLA) . (pp. 1049-1054). Cancun: IEEE.

N. M. António et al.,  "Predicting hotel bookings cancellation with a machine learning classification model", in 16th IEEE Int. Conf. on Machine Learning and Applications (ICMLA) , Cancun, IEEE, 2017, pp. 1049-1054

@inproceedings{antónio2017_1732211397536,
	author = "Antonio, N. and de Almeida, A. and Nunes, L.",
	title = "Predicting hotel bookings cancellation with a machine learning classification model",
	booktitle = "16th IEEE International Conference on Machine Learning and Applications (ICMLA) ",
	year = "2017",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.1109/ICMLA.2017.00-11",
	pages = "1049-1054",
	publisher = "IEEE",
	address = "Cancun",
	organization = "IEEE",
	url = "https://ieeexplore.ieee.org/document/8260781/"
}

TY  - CPAPER
TI  - Predicting hotel bookings cancellation with a machine learning classification model
T2  - 16th IEEE International Conference on Machine Learning and Applications (ICMLA) 
AU  - Antonio, N.
AU  - de Almeida, A.
AU  - Nunes, L.
PY  - 2017
SP  - 1049-1054
DO  - 10.1109/ICMLA.2017.00-11
CY  - Cancun
UR  - https://ieeexplore.ieee.org/document/8260781/
AB  - Booking cancellations have significant impact on demand-management decisions in the hospitality industry. To mitigate the effect of cancellations, hotels implement rigid cancellation policies and overbooking tactics, which in turn can have a negative impact on revenue and on the hotel reputation. To reduce this impact, a machine learning based system prototype was developed. It makes use of the hotel’s Property Management Systems data and trains a classification model every day to predict
which bookings are “likely to cancel” and with that calculate net demand. This prototype, deployed in a production environment in two hotels, by enforcing A/B testing, also enables the measurement of the impact of actions taken to act upon bookings predicted as “likely to cancel”. Results indicate good prototype performance and provide important indications for research progress whilst evidencing that bookings contacted by hotels cancel less than bookings not contacted.
ER  -