Repository logo
 
Publication

Profiling and Rating Prediction from Multi-Criteria Crowd-Sourced Hotel Ratings

2017Conference object Open access
Simple item page
dc.contributor.authorLeal, Fátima
dc.contributor.authorGonzález-Veléz, Horacio
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorBurguillo, Juan Carlos
dc.date.accessioned2017-08-28T14:22:30Z
dc.date.available2017-08-28T14:22:30Z
dc.date.issued2017
dc.date.updated2017-08-21T18:07:35Z
dc.descriptionECMS 2017- 31st European Conference on Modelling and Simulation - May 23rd - May 26th, 2017 Budapest, Hungarypt_PT
dc.description.abstractBased on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating – single criterion (SC) profiling – or on the multiple ratings available – multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (ı) the selection of the most representative crowd-sourced rating; and (ıı) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.7148/2017-0576pt_PT
dc.identifier.doi10.7148/2017-0576pt_PT
dc.identifier.isbn9780993244049
dc.identifier.urihttp://hdl.handle.net/10400.22/10208
dc.language.isoengpt_PT
dc.relation.ispartofseriesECMS;2017
dc.subjectCollaborative Filteringpt_PT
dc.subjectPersonalisationpt_PT
dc.subjectPrediction Modelspt_PT
dc.subjectMulti-criteria Ratingspt_PT
dc.subjectTourismpt_PT
dc.subjectCrowd-Sourcingpt_PT
dc.subjectRecommender Systemspt_PT
dc.subjectData Analyticspt_PT
dc.titleProfiling and Rating Prediction from Multi-Criteria Crowd-Sourced Hotel Ratingspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceMay 23rd - May 26th, 2017 Budapest, Hungarypt_PT
oaire.citation.title31st European Conference on Modelling and Simulationpt_PT
person.familyNameBENEDITA CAMPOS NEVES MALHEIRO
person.givenNameMARIA
person.identifier.ciencia-id7A15-08FC-4430
person.identifier.orcid0000-0001-9083-4292
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationbabd4fda-654a-4b59-952d-6113eebbb308
relation.isAuthorOfPublication.latestForDiscoverybabd4fda-654a-4b59-952d-6113eebbb308

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
COM_LSA_BeneditaMalheiro_ECMS_2017.pdf
Size:
1.02 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP – LSA – Comunicações em eventos científicos