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dc.contributor.authorMartín Fuentes, Eva
dc.contributor.authorFernàndez Camon, César
dc.contributor.authorMateu Piñol, Carles
dc.contributor.authorMariné Roig, Estela
dc.date.accessioned2018-09-10T07:22:43Z
dc.date.available2020-10-24T22:23:32Z
dc.date.issued2018
dc.identifier.issn0278-4319
dc.identifier.urihttp://hdl.handle.net/10459.1/64698
dc.description.abstractThis 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.ca_ES
dc.description.sponsorshipThis 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).ca_ES
dc.language.isoengca_ES
dc.publisherElsevierca_ES
dc.relationMINECO/PN2013-2016/TIN2015-71799-C2-2-Pca_ES
dc.relationMINECO/PN2013-2016/ENE2015-64117-C5-1-Rca_ES
dc.relation.isformatofVersió postprint del document publicat a https://doi.org/10.1016/j.ijhm.2017.10.016ca_ES
dc.relation.ispartofInternational Journal of Hospitality Management, 2018, vol. 69, p. 75-83ca_ES
dc.rightscc-by-nc-nd (c) Elsevier, 2018ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAirbnbca_ES
dc.subjectHotel classification systemca_ES
dc.subjectSupport vector machineca_ES
dc.subjectBig dataca_ES
dc.subjectPeer-to-peer accommodation platformca_ES
dc.titleModelling a grading scheme for peer-to-peer accommodation: Stars for Airbnbca_ES
dc.typeinfo:eu-repo/semantics/articleca_ES
dc.identifier.idgrec026059
dc.type.versioninfo:eu-repo/semantics/acceptedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1016/j.ijhm.2017.10.016


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cc-by-nc-nd (c) Elsevier, 2018
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Elsevier, 2018