The Benefits of Automated Machine Learning in Hospitality

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2022
Outros Autores: Pinto, Diego Costa, Shuqair, Saleh, Montali, Davide, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/143844
Resumo: Castelli, M., Pinto, D. C., Shuqair, S., Montali, D., & Vanneschi, L. (2022). The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool. Emerging Science Journal, 6(6), 1237-1254. https://doi.org/10.28991ESJ-2022-06-06-02. Funding:This study was supported by grant DSAIPA/DS/0113/2019 from FCT (Fundação para a Ciência e a Tecnologia), Portugal. This work was also supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
id RCAP_1b663485e7c50e7dca69e6600f4e866a
oai_identifier_str oai:run.unl.pt:10362/143844
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling The Benefits of Automated Machine Learning in HospitalityA Step-By-Step Guide and AutoML ToolArtificial IntelligenceAutomated Machine LearningBehavioral ResearchHospitalityGeneralSDG 8 - Decent Work and Economic GrowthCastelli, M., Pinto, D. C., Shuqair, S., Montali, D., & Vanneschi, L. (2022). The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool. Emerging Science Journal, 6(6), 1237-1254. https://doi.org/10.28991ESJ-2022-06-06-02. Funding:This study was supported by grant DSAIPA/DS/0113/2019 from FCT (Fundação para a Ciência e a Tecnologia), Portugal. This work was also supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNCastelli, MauroPinto, Diego CostaShuqair, SalehMontali, DavideVanneschi, Leonardo2022-09-19T22:25:54Z2022-12-012022-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18application/pdfhttp://hdl.handle.net/10362/143844eng2610-9182PURE: 46625772https://doi.org/10.28991/ESJ-2022-06-06-02info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:22:37Zoai:run.unl.pt:10362/143844Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:51:10.189001Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The Benefits of Automated Machine Learning in Hospitality
A Step-By-Step Guide and AutoML Tool
title The Benefits of Automated Machine Learning in Hospitality
spellingShingle The Benefits of Automated Machine Learning in Hospitality
Castelli, Mauro
Artificial Intelligence
Automated Machine Learning
Behavioral Research
Hospitality
General
SDG 8 - Decent Work and Economic Growth
title_short The Benefits of Automated Machine Learning in Hospitality
title_full The Benefits of Automated Machine Learning in Hospitality
title_fullStr The Benefits of Automated Machine Learning in Hospitality
title_full_unstemmed The Benefits of Automated Machine Learning in Hospitality
title_sort The Benefits of Automated Machine Learning in Hospitality
author Castelli, Mauro
author_facet Castelli, Mauro
Pinto, Diego Costa
Shuqair, Saleh
Montali, Davide
Vanneschi, Leonardo
author_role author
author2 Pinto, Diego Costa
Shuqair, Saleh
Montali, Davide
Vanneschi, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Pinto, Diego Costa
Shuqair, Saleh
Montali, Davide
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Artificial Intelligence
Automated Machine Learning
Behavioral Research
Hospitality
General
SDG 8 - Decent Work and Economic Growth
topic Artificial Intelligence
Automated Machine Learning
Behavioral Research
Hospitality
General
SDG 8 - Decent Work and Economic Growth
description Castelli, M., Pinto, D. C., Shuqair, S., Montali, D., & Vanneschi, L. (2022). The Benefits of Automated Machine Learning in Hospitality: A Step-By-Step Guide and AutoML Tool. Emerging Science Journal, 6(6), 1237-1254. https://doi.org/10.28991ESJ-2022-06-06-02. Funding:This study was supported by grant DSAIPA/DS/0113/2019 from FCT (Fundação para a Ciência e a Tecnologia), Portugal. This work was also supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-19T22:25:54Z
2022-12-01
2022-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/143844
url http://hdl.handle.net/10362/143844
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2610-9182
PURE: 46625772
https://doi.org/10.28991/ESJ-2022-06-06-02
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799138106758135808

Registros relacionados