Data science in supporting hotel management: Application of predictive models to Booking.com guest evaluations

dc.contributor.authorMartins, Ana Filipa
dc.contributor.authorSilva, Luís M.
dc.contributor.authorMarques, Jorge
dc.date.accessioned2024-04-09T13:57:42Z
dc.date.available2024-04-09T13:57:42Z
dc.date.issued2024-03-15
dc.description.abstractData science is a multidisciplinary area that gathers several branches, such as statistics, databases, and computer science and whose importance has become more substantial over the last few years. Using several techniques and algorithms from machine learning allows us to understand how certain variables are related, as well as to visualize data and make predictions. This paper aims to use data science as a strategic instrument for the hospitality industry by proposing a model that can help to predict which characteristics will be more valued by guests. By better understanding which features guests value most when evaluating a stay at a hotel, it will be easier for hotel managers to make informed decisions about which service operations management strategies should be used. It can also be helpful in terms of investment decisions, as it can indicate which aspects will be most important to value in a hotel. In this research, it was possible to conclude that guests’ ratings are related primarily to the commodities available at the hotels, followed by cleanliness, staff, location, price-quality relation, and comfort.
dc.identifier.citationMartins, A. F., Silva, L. M., & Marques, J. (2024). Data Science in Supporting Hotel Management: Application of Predictive Models to Booking.com Guest Evaluations. In J. V. Carvalho, A. Abreu, D. Liberato, J. A. D. Rebolledo (Eds.), Advances in Tourism, Technology and Systems. ICOTTS 2023. Smart Innovation, Systems and Technologies, (vol. 384, pp. 51-59). Springer. https://doi.org/10.1007/978-981-99-9758-9_5. Repositório Institucional UPT. https://hdl.handle.net/11328/5581
dc.identifier.isbn978-981-99-9882-1
dc.identifier.isbn978-981-99-9758-9
dc.identifier.urihttps://hdl.handle.net/11328/5581
dc.language.isoeng
dc.publisherSpringer
dc.relation.hasversionhttps://doi.org/10.1007/978-981-99-9758-9_5
dc.rightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectData science
dc.subjectHotels
dc.subjectHotel management
dc.subjectHospitality
dc.subjectRating systems
dc.subject.fosCiências Sociais - Outras Ciências Sociais
dc.titleData science in supporting hotel management: Application of predictive models to Booking.com guest evaluations
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage59
oaire.citation.startPage51
oaire.citation.titleAdvances in Tourism, Technology and Systems. ICOTTS 2023. Smart Innovation, Systems and Technologies
oaire.citation.volume384
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameMarques
person.givenNameJorge
person.identifier.ciencia-id7A1C-388B-D278
person.identifier.orcid0000-0001-5392-5128
person.identifier.ridE-6336-2016
person.identifier.scopus-author-id57189515439
relation.isAuthorOfPublicationa3167d55-dd12-46b8-89fc-6502fc4c7542
relation.isAuthorOfPublication.latestForDiscoverya3167d55-dd12-46b8-89fc-6502fc4c7542

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