| dc.contributor.author | Leal, Fátima | |
| dc.contributor.author | Veloso, Bruno | |
| dc.contributor.author | Malheiro, Benedita | |
| dc.contributor.author | Juan Carlos, Burguillo | |
| dc.date.accessioned | 2021-10-13T12:37:08Z | |
| dc.date.embargo | 2031 | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Crowdsourced data streams are continuous flows of data generated at high rate by users, also known as the crowd. These data streams are popular and extremely valuable in several domains. This is the case of tourism, where crowdsourcing platforms rely on tourist and business inputs to provide tailored recommendations to future tourists in real time. The continuous, open and non-curated nature of the crowd-originated data requires robust data stream mining techniques for on-line profiling, recommendation and evaluation. The sought techniques need, not only, to continuously improve profiles and learn models, but also be transparent, overcome biases, prioritise preferences, and master huge data volumes; all in real time. This article surveys the state-of-art in this field, and identifies future research opportunities. | pt_PT |
| dc.description.sponsorship | This work was partially financed by National Funds through the FCT – Funda¸c˜ao para a Ciˆencia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UIDB/50014/2020, and also from Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF). | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1007/978-3-030-72657-7_25 | pt_PT |
| dc.identifier.isbn | 978-3-030-72656-7 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/18701 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation | INESC TEC- Institute for Systems and Computer Engineering, Technology and Science | |
| dc.relation.ispartofseries | 1365;25 | |
| dc.relation.publisherversion | https://link.springer.com/chapter/10.1007%2F978-3-030-72657-7_25 | pt_PT |
| dc.subject | Crowdsourced data streams | pt_PT |
| dc.subject | Data stream mining | pt_PT |
| dc.subject | Profiling | pt_PT |
| dc.subject | Recommendation | pt_PT |
| dc.subject | Tourism | pt_PT |
| dc.title | Crowdsourced Data Stream Mining for Tourism Recommendation | pt_PT |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | INESC TEC- Institute for Systems and Computer Engineering, Technology and Science | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT | |
| oaire.citation.endPage | 269 | pt_PT |
| oaire.citation.startPage | 260 | pt_PT |
| oaire.citation.title | Progress in Artificial Intelligence. EPIA 2021 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | BENEDITA CAMPOS NEVES MALHEIRO | |
| person.givenName | MARIA | |
| person.identifier.ciencia-id | 7A15-08FC-4430 | |
| person.identifier.orcid | 0000-0001-9083-4292 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
| rcaap.type | bookPart | pt_PT |
| relation.isAuthorOfPublication | babd4fda-654a-4b59-952d-6113eebbb308 | |
| relation.isAuthorOfPublication.latestForDiscovery | babd4fda-654a-4b59-952d-6113eebbb308 | |
| relation.isProjectOfPublication | 7a2d9a82-ee07-4c57-bbbf-2d88b942688d | |
| relation.isProjectOfPublication.latestForDiscovery | 7a2d9a82-ee07-4c57-bbbf-2d88b942688d |
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