Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb

This study aims, firstly, to determine whether hotel categories worldwide can be inferred from features that are not taken into account by the institutions in charge of assigning such categories and, if so, to create a model to classify the properties offered by P2P accommodation platforms, similar...

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Detalles Bibliográficos
Autores: Martín Fuentes, Eva, Fernàndez Camon, César, Mateu Piñol, Carles, Mariné Roig, Estela
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2018
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/64698
Acceso en línea:https://doi.org/10.1016/j.ijhm.2017.10.016
http://hdl.handle.net/10459.1/64698
Access Level:acceso abierto
Palabra clave:Airbnb
Hotel classification system
Support vector machine
Big data
Peer-to-peer accommodation platform
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spelling Modelling a grading scheme for peer-to-peer accommodation: Stars for AirbnbMartín Fuentes, EvaFernàndez Camon, CésarMateu Piñol, CarlesMariné Roig, EstelaAirbnbHotel classification systemSupport vector machineBig dataPeer-to-peer accommodation platformThis study aims, firstly, to determine whether hotel categories worldwide can be inferred from features that are not taken into account by the institutions in charge of assigning such categories and, if so, to create a model to classify the properties offered by P2P accommodation platforms, similar to grading scheme categories for hotels, thus preventing opportunistic behaviours of information asymmetry and information overload. The characteristics of 33,000 hotels around the world and 18,000,000 reviews from Booking.com were collected automatically and, using the Support Vector Machine classification technique, we trained a model to assign a category to a given hotel. The results suggest that a hotel classification can usually be inferred by different criteria (number of reviews, price, score, and users’ wish lists) that have nothing to do with the official criteria. Moreover, room prices are the most important feature for predicting the hotel category, followed by cleanliness and location.This work was partially funded by the Spanish Ministry of the Economy and Competitiveness: research project TIN2015-71799-C2-2-P and ENE2015-64117-C5-1-R. This research article has received a grant for its linguistic revision from the Language Institute of the University of Lleida (2017 call).Elsevier2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://doi.org/10.1016/j.ijhm.2017.10.016http://hdl.handle.net/10459.1/64698reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-Pinfo:eu-repo/grantAgreement/MINECO//ENE2015-64117-C5-1-RVersió postprint del document publicat a https://doi.org/10.1016/j.ijhm.2017.10.016International Journal of Hospitality Management, 2018, vol. 69, p. 75-83cc-by-nc-nd (c) Elsevier, 2018info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repositori.udl.cat:10459.1/646982026-06-24T12:42:17Z
dc.title.none.fl_str_mv Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
title Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
spellingShingle Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
Martín Fuentes, Eva
Airbnb
Hotel classification system
Support vector machine
Big data
Peer-to-peer accommodation platform
title_short Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
title_full Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
title_fullStr Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
title_full_unstemmed Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
title_sort Modelling a grading scheme for peer-to-peer accommodation: Stars for Airbnb
dc.creator.none.fl_str_mv Martín Fuentes, Eva
Fernàndez Camon, César
Mateu Piñol, Carles
Mariné Roig, Estela
author Martín Fuentes, Eva
author_facet Martín Fuentes, Eva
Fernàndez Camon, César
Mateu Piñol, Carles
Mariné Roig, Estela
author_role author
author2 Fernàndez Camon, César
Mateu Piñol, Carles
Mariné Roig, Estela
author2_role author
author
author
dc.subject.none.fl_str_mv Airbnb
Hotel classification system
Support vector machine
Big data
Peer-to-peer accommodation platform
topic Airbnb
Hotel classification system
Support vector machine
Big data
Peer-to-peer accommodation platform
description This study aims, firstly, to determine whether hotel categories worldwide can be inferred from features that are not taken into account by the institutions in charge of assigning such categories and, if so, to create a model to classify the properties offered by P2P accommodation platforms, similar to grading scheme categories for hotels, thus preventing opportunistic behaviours of information asymmetry and information overload. The characteristics of 33,000 hotels around the world and 18,000,000 reviews from Booking.com were collected automatically and, using the Support Vector Machine classification technique, we trained a model to assign a category to a given hotel. The results suggest that a hotel classification can usually be inferred by different criteria (number of reviews, price, score, and users’ wish lists) that have nothing to do with the official criteria. Moreover, room prices are the most important feature for predicting the hotel category, followed by cleanliness and location.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.ijhm.2017.10.016
http://hdl.handle.net/10459.1/64698
url https://doi.org/10.1016/j.ijhm.2017.10.016
http://hdl.handle.net/10459.1/64698
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MINECO//TIN2015-71799-C2-2-P
info:eu-repo/grantAgreement/MINECO//ENE2015-64117-C5-1-R
Versió postprint del document publicat a https://doi.org/10.1016/j.ijhm.2017.10.016
International Journal of Hospitality Management, 2018, vol. 69, p. 75-83
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier, 2018
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier, 2018
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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