Publication
On-line guest profiling and hotel recommendation
| dc.contributor.author | Veloso, Bruno | |
| dc.contributor.author | Leal, Fátima | |
| dc.contributor.author | Malheiro, Benedita | |
| dc.contributor.author | Burguillo, Juan Carlos | |
| dc.date.accessioned | 2019-03-12T11:56:05Z | |
| dc.date.embargo | 2119 | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2019-03-08T15:52:54Z | |
| dc.description.abstract | Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD) matrix factorisation algorithm for rating prediction. Additionally, we explore different: (i) profiling approaches (hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together with location and StV post-recommendation filtering. While the most promising results occur with the hotelbased version, the best theme-based version shows a remarkable memory conciseness when compared with its hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams. The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier | 15674223 | en_US |
| dc.identifier.doi | 10.1016/j.elerap.2019.100832 | pt_PT |
| dc.identifier.issn | 15674223 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/12971 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1567422319300092 | pt_PT |
| dc.subject | Profiling | pt_PT |
| dc.subject | Recommendation | pt_PT |
| dc.subject | Data streams | pt_PT |
| dc.subject | Post-filtering | pt_PT |
| dc.title | On-line guest profiling and hotel recommendation | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Electronic Commerce Research and Applications | pt_PT |
| oaire.citation.volume | 34 | pt_PT |
| person.familyName | BENEDITA CAMPOS NEVES MALHEIRO | |
| person.givenName | MARIA | |
| person.identifier.ciencia-id | 7A15-08FC-4430 | |
| person.identifier.orcid | 0000-0001-9083-4292 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | babd4fda-654a-4b59-952d-6113eebbb308 | |
| relation.isAuthorOfPublication.latestForDiscovery | babd4fda-654a-4b59-952d-6113eebbb308 |