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Advances in Computer and Communication

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Article Open Access http://dx.doi.org/10.26855/acc.2023.12.003

Research on Hotel Reservation Scheme Based on Random Forest Model Prediction

Junchao Zhang, Dong Li, Huiling Lan, Lei Tang, Mengmeng Guo*

Guilin University of Technology at Nanning, Chongzuo, Guangxi, China.

*Corresponding author: Mengmeng Guo

Published: January 17,2024

Abstract

The X Hotel in a certain city is mainly used for conferences and tourists. Guest rooms are reserved by telephone or the Internet. This kind of reservation has great uncertainty, and customers are likely to cancel the reservation due to various reasons. The May Day holiday is approaching. In order to strive for greater profits, Hotel X must win customers on the one hand, and reduce the losses suffered by customers who cancel reservations on the other hand. To this end, Hotel X has adopted a number of measures. First of all, the guest room is required to prepay the first day's rent as a deposit. If the customer cancels the reservation before noon of the previous day, the deposit will be refunded in full, otherwise, the deposit will be forfeited. Secondly, Hotel X adopts variable prices and adjusts prices according to market demand. Generally speaking, the prices in peak tourist seasons are relatively high, and the prices in off-seasons are slightly lower. Then use the random forest model to predict the booking demand in a specific period in the future, which will help the hotel to adjust the room rate reasonably. Establishing a reservation cancellation prediction model to predict the probability of a customer canceling a reservation under certain conditions will help the hotel adjust the reservation limit and cancellation policy.

References

[1] Lan, H. & Pan, Y. (2019, June). A crowdsourcing quality prediction model based on random forests. In 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), (pp. 315-319). IEEE.

[2] Subramanian, R. R., Reddy, M. P., Kousik, K., Rupesh, S., Rohith, S., & Kumar, B. S. (2022, December). ClassHotel: Application of data analytic techniques for online hotel recommendation. In 2022 International Conference on Automation, Computing and Re-newable Systems (ICACRS), (pp. 1021-1026). IEEE.

[3] Gómez-Ramírez, J., Ávila-Villanueva, M., & Fernández-Blázquez, M. Á. (2020). Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Scientific Re-ports, 10(1), 1-15.

[4] Thomas, E., Ferrer, A. G., Lardeux, B., Boudia, M., Haas-Frangii, C., & Agost, R. A. (2019). Cascaded machine learning model for efficient hotel recommendations from air travel bookings. In Proceedings of Proceedings of the 12th ACM Conference on Recommender Systems, ACM RecSys Workshop on Recommenders in Tourism (RecTour 2019 vol 2435). CEUR Workshop Proceedings Copenhagen (pp. 9-16).

[5] Dai, P., Chang, W., Xin, Z., Cheng, H., Ouyang, W., & Luo, A. (2021). A retrospective study on the influencing factors and prediction of hospitalization expenses for chronic renal failure in China based on random forest and LASSO regression. Frontiers in Public Health, 9, 678276.

[6] Lee, M., Kwon, W., & Back, K. J. (2021). Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making. International Journal of Contemporary Hospitality Management, 33(6), 2117-2136.

[7] Tekin, A. T., & Cebi, F. (2020). Click and sales prediction for digital advertisements: Real world application for otas. In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019 (pp. 205-212). Springer International Publishing.

[8] El Hamdaoui, H., Boujraf, S., El Houda Chaoui, N., Alami, B., & Maaroufi, M. (2021). Improving heart disease prediction using random forest and adaboost algorithms. International Journal of Online & Biomedical Engineering, 17(11).

[9] Wang, Y., Huang, C., Zhao, M., Hou, J., Zhang, Y., & Gu, J. (2020). Mapping the population density in mainland China using NPP/VIIRS and points-of-interest data based on a random forests model. Remote Sensing, 12(21), 3645.

[10] Abdulkareem, K. H., Mohammed, M. A., Salim, A., Arif, M., Geman, O., Gupta, D., & Khanna, A. (2021). Realizing an effective COVID-19 diagnosis system based on machine learning and IOT in smart hospital environment. IEEE Internet of Things Journal, 8(21), 15919-15928.

[11] Cakmak, T., Tekin, A., Senel, C., Coban, T., Uran, Z. E., & Sakar, C. O. (2019). Accurate prediction of advertisement clicks based on impression and click-through rate using extreme gradient boosting. In ICPRAM (pp. 621-629).

[12] Avdeef, A., & Kansy, M. (2022). Predicting solubility of newly approved drugs (2016–2020) with a simple ABSOLV and GSE (Flexible-Acceptor) consensus model outperforming random forest regression. Journal of Solution Chemistry, 51(9), 1020-1055.

How to cite this paper

Research on Hotel Reservation Scheme Based on Random Forest Model Prediction

How to cite this paper: Junchao Zhang, Dong Li, Huiling Lan, Lei Tang, Mengmeng Guo. (2023) Research on Hotel Reservation Scheme Based on Random Forest Model Prediction. Advances in Computer and Communication4(6), 358-362.

DOI: http://dx.doi.org/10.26855/acc.2023.12.003