A 2020 perspective on “Online guest profiling and hotel recommendation”: Reliability, scalability, traceability and transparency

dc.contributor.authorVeloso, Bruno
dc.contributor.authorLeal, Fátima
dc.contributor.authorMalheiro, Benedita
dc.contributor.authorBurguillo, Juan Carlos
dc.date.accessioned2022-04-28T11:24:01Z
dc.date.available2022-04-28T11:24:01Z
dc.date.issued2020-04
dc.description.abstractTourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.pt_PT
dc.identifier.citationVeloso, B., Leal, F., Malheiro, B., & Burguillo, J. C. (2020). A 2020 perspective on “Online guest profiling and hotel recommendation”: Reliability, scalability, traceability and transparency. Electronic Commerce Research and Applications, 40(March–April 2020), 100957. https://doi.org/10.1016/j.elerap.2020.100957. Repositório Institucional UPT. http://hdl.handle.net/11328/4051pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.elerap.2020.100957pt_PT
dc.identifier.issn1567-4223 (Print)
dc.identifier.urihttp://hdl.handle.net/11328/4051
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S156742232030034Xpt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectData stream miningpt_PT
dc.subjectProfilingpt_PT
dc.subjectRecommendationpt_PT
dc.subjectPost-filteringpt_PT
dc.titleA 2020 perspective on “Online guest profiling and hotel recommendation”: Reliability, scalability, traceability and transparencypt_PT
dc.typejournal articlept_PT
degois.publication.firstPage100957pt_PT
degois.publication.titleElectronic Commerce Research and Applicationspt_PT
degois.publication.volume40pt_PT
dspace.entity.typePublicationen
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