Crowdsourced data stream mining for tourism recommendation

dc.contributor.authorLeal, Fátima
dc.contributor.authorVeloso, Bruno
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorBurguillo, Juan C.
dc.date.accessioned2021-04-29T14:43:16Z
dc.date.available2021-04-29T14:43:16Z
dc.date.issued2021-04
dc.description.abstractCrowdsourced 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.identifier.citationLeal F., Veloso B., Malheiro B.,& Burguillo J.C. (2021). Crowdsourced Data Stream Mining for Tourism Recommendation. In: Rocha Á., Adeli H., Dzemyda G., Moreira F., & Ramalho Correia A.M. (eds) Trends and Applications in Information Systems and Technologies, WorldCIST 2021. Advances in Intelligent Systems and Computing (1365, pp. 160-169). Doi:10.1007/978-3-030-72657-7_25. Disponível no Repositório UPT, http://hdl.handle.net/11328/3502pt_PT
dc.identifier.doi10.1007/978-3-030-72657-7_25pt_PT
dc.identifier.isbn978-3-030-72657-7
dc.identifier.urihttp://hdl.handle.net/11328/3502
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-72657-7_25#citeaspt_PT
dc.rightsopen accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCrowdsourced data streamspt_PT
dc.subjectData stream miningpt_PT
dc.subjectProfilingpt_PT
dc.subjectRecommendationpt_PT
dc.subjectTourismpt_PT
dc.titleCrowdsourced data stream mining for tourism recommendationpt_PT
dc.typeconferenceObjectpt_PT
degois.publication.firstPage260pt_PT
degois.publication.lastPage269pt_PT
degois.publication.titleWorld Conference on Information Systems and Technologies WorldCIST 2021pt_PT
degois.publication.volume1365pt_PT
dspace.entity.typePublicationen
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