A hybrid machine learning approach to hotel sales rank prediction

Praveen Ranjan Srivastava, Prajwal Eachempati, Vincent Charles and Nripendra P. Rana

Journal of the Operational Research Society, 2023, vol. 74, issue 6, 1407-1423

Abstract: One of the challenges that the hospitality and tourism industry faces is determining the best-rated and ideal hotels for people with customized preferences. Users belong to various demographic groups, and the factors they consider when selecting a hotel depend on their priorities at the time. Therefore, to provide appropriate recommendations tailored to the individual preferences of users, forecasting customer demand is required, for which hotel sales rank prediction models are to be developed. In this regard, the present paper aims to develop a customized hotel recommendation model for sales rank prediction that considers factors like distance from a strategic location, online user ratings, word-of-mouth rating, hotel tariff, and customer reviews, using the aggregated data set of Indian hotels from trivago.com. Results show that the Artificial Neural Network algorithm predicts sales rank better than the Random Forest and Gradient Boosting algorithms. Implications for practice are provided.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2022.2096498 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:74:y:2023:i:6:p:1407-1423

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2022.2096498

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().