Stream-based explainable recommendations via blockchain profiling
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/19272 |
Resumo: | Explainable 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 pro filing. 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. |
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Stream-based explainable recommendations via blockchain profilingRecommendation SystemsExplainabilityBlockchainData StreamsHistorical Profi lingCrowdsourcingIntelligent Information SystemsExplainable 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 pro filing. 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.This work was partially financed by: (i) the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation – COMPETE 2020 Programme within project «POCI-01-0145- FEDER-006961», and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020; (ii) the Xunta de Galicia (Centro singular de investigaci´on de Galicia accreditation 2019-2022, also financed from ERDF); and (iii) the Irish Research Council within the 16 F. Leal et al. / Stream-based Explainable Recommendations via Blockchain Profiling framework of the EU ERA-NET CHIST-ERA project SPuMoNI: Smart Pharmaceutical Manufacturing www.spumoni.eu.IOS PressRepositório Científico do Instituto Politécnico do PortoLeal, FátimaVeloso, BrunoMalheiro, BeneditaBurguillo, Juan CarlosChis, Adriana E.González–Vélez, Horacio20212031-12-28T00:00:00Z2021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/19272eng1069-250910.3233/ICA-210668metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:11:25Zoai:recipp.ipp.pt:10400.22/19272Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:38:46.177562Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Stream-based explainable recommendations via blockchain profiling |
title |
Stream-based explainable recommendations via blockchain profiling |
spellingShingle |
Stream-based explainable recommendations via blockchain profiling Leal, Fátima Recommendation Systems Explainability Blockchain Data Streams Historical Profi ling Crowdsourcing Intelligent Information Systems |
title_short |
Stream-based explainable recommendations via blockchain profiling |
title_full |
Stream-based explainable recommendations via blockchain profiling |
title_fullStr |
Stream-based explainable recommendations via blockchain profiling |
title_full_unstemmed |
Stream-based explainable recommendations via blockchain profiling |
title_sort |
Stream-based explainable recommendations via blockchain profiling |
author |
Leal, Fátima |
author_facet |
Leal, Fátima Veloso, Bruno Malheiro, Benedita Burguillo, Juan Carlos Chis, Adriana E. González–Vélez, Horacio |
author_role |
author |
author2 |
Veloso, Bruno Malheiro, Benedita Burguillo, Juan Carlos Chis, Adriana E. González–Vélez, Horacio |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Leal, Fátima Veloso, Bruno Malheiro, Benedita Burguillo, Juan Carlos Chis, Adriana E. González–Vélez, Horacio |
dc.subject.por.fl_str_mv |
Recommendation Systems Explainability Blockchain Data Streams Historical Profi ling Crowdsourcing Intelligent Information Systems |
topic |
Recommendation Systems Explainability Blockchain Data Streams Historical Profi ling Crowdsourcing Intelligent Information Systems |
description |
Explainable 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 pro filing. 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z 2031-12-28T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/19272 |
url |
http://hdl.handle.net/10400.22/19272 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1069-2509 10.3233/ICA-210668 |
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metadata only access info:eu-repo/semantics/openAccess |
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metadata only access |
eu_rights_str_mv |
openAccess |
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application/pdf |
dc.publisher.none.fl_str_mv |
IOS Press |
publisher.none.fl_str_mv |
IOS Press |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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