Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3883
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dc.contributor.authorLeal, Fátima-
dc.contributor.authorVeloso, Bruno-
dc.contributor.authorMalheiro, Benedita-
dc.contributor.authorBurguillo, Juan C.-
dc.contributor.authorChis, Adriana E.-
dc.contributor.authorGonzález-Vélez, Horacio-
dc.date.accessioned2022-01-14T11:07:56Z-
dc.date.available2022-01-14T11:07:56Z-
dc.date.issued2022-
dc.identifier.citationLeal, F., Veloso, B., Malheiro, B., Burguillo, J. C., Chis, A. E., & González-Vélez, H. (2022). Stream-based explainable recommendations via blockchain profiling. Integrated Computer-Aided Engineering, 29(2022), 105-121. doi: 10.3233/ICA-210668. Disponível no Repositório UPT, http://hdl.handle.net/11328/3883pt_PT
dc.identifier.issn1069-2509-
dc.identifier.urihttp://hdl.handle.net/11328/3883-
dc.description.abstractExplainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.pt_PT
dc.language.isoengpt_PT
dc.publisherIOS Presspt_PT
dc.rightsrestrictedAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRecommendation systemspt_PT
dc.subjectExplainabilitypt_PT
dc.subjectBlockchainpt_PT
dc.subjectData streamspt_PT
dc.subjectHistorical profilingpt_PT
dc.subjectCrowdsourcingpt_PT
dc.subjectIntelligent information systemspt_PT
dc.titleStream-based explainable recommendations via blockchain profilingpt_PT
dc.typearticlept_PT
dc.relation.publisherversionhttps://content.iospress.com/articles/integrated-computer-aided-engineering/ica210668pt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage105pt_PT
degois.publication.lastPage121pt_PT
degois.publication.volume29pt_PT
degois.publication.titleIntegrated Computer-Aided Engineeringpt_PT
dc.identifier.doi10.3233/ICA-210668pt_PT
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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