Quality web information retrieval : towards improving semantic recommender systems with friendsourcing
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/224903 |
Resumo: | Web content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project. |
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Díaz, AliciaMotz, ReginaFernandez, AlejandroLima, Jose Valdeni deLópez, Diego M.2021-08-03T04:30:35Z20111519-132Xhttp://hdl.handle.net/10183/224903000819551Web content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project.application/pdfengCadernos de informática. Porto Alegre. Vol. 6, n. 1 (maio 2011), p. 289-292Recuperacao : InformacaoOntologiasRecommender systemsCollaborative filteringSocial networkFriendsourcingOntologyQuality web information retrieval : towards improving semantic recommender systems with friendsourcinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT000819551.pdf.txt000819551.pdf.txtExtracted Texttext/plain23000http://www.lume.ufrgs.br/bitstream/10183/224903/2/000819551.pdf.txta648271dd9b0993137c416a894323789MD52ORIGINAL000819551.pdfTexto completo (inglês)application/pdf52767http://www.lume.ufrgs.br/bitstream/10183/224903/1/000819551.pdf56df683066577c651305ff8b55affb92MD5110183/2249032021-08-18 04:30:01.588846oai:www.lume.ufrgs.br:10183/224903Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-08-18T07:30:01Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
title |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
spellingShingle |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing Díaz, Alicia Recuperacao : Informacao Ontologias Recommender systems Collaborative filtering Social network Friendsourcing Ontology |
title_short |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
title_full |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
title_fullStr |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
title_full_unstemmed |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
title_sort |
Quality web information retrieval : towards improving semantic recommender systems with friendsourcing |
author |
Díaz, Alicia |
author_facet |
Díaz, Alicia Motz, Regina Fernandez, Alejandro Lima, Jose Valdeni de López, Diego M. |
author_role |
author |
author2 |
Motz, Regina Fernandez, Alejandro Lima, Jose Valdeni de López, Diego M. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Díaz, Alicia Motz, Regina Fernandez, Alejandro Lima, Jose Valdeni de López, Diego M. |
dc.subject.por.fl_str_mv |
Recuperacao : Informacao Ontologias |
topic |
Recuperacao : Informacao Ontologias Recommender systems Collaborative filtering Social network Friendsourcing Ontology |
dc.subject.eng.fl_str_mv |
Recommender systems Collaborative filtering Social network Friendsourcing Ontology |
description |
Web content quality is crucial in any domains, but it is even more critical in the health and e-learning ones. Users need to retrieve information that is precise, believable, and relevant to their problem. With the exponential growth of web contents, Recommender System has become indispensable for discovering quality information that might interest or be needed by web users. Quality-based Recommender Systems take into account quality criteria like credibility, believability, readability. In this paper, we present an approach to conceive Social Semantic Recommender Systems. In this approach a friendsourcing strategy is applied to better adequate recommendations to the user needs. The friendsourcing strategy focuses on the use of social force to assess quality of web content. In this paper we introduce the main research issues of this approach and detail the road-map we are following in the QHIR Project. |
publishDate |
2011 |
dc.date.issued.fl_str_mv |
2011 |
dc.date.accessioned.fl_str_mv |
2021-08-03T04:30:35Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/other |
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info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/224903 |
dc.identifier.issn.pt_BR.fl_str_mv |
1519-132X |
dc.identifier.nrb.pt_BR.fl_str_mv |
000819551 |
identifier_str_mv |
1519-132X 000819551 |
url |
http://hdl.handle.net/10183/224903 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Cadernos de informática. Porto Alegre. Vol. 6, n. 1 (maio 2011), p. 289-292 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
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UFRGS |
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Repositório Institucional da UFRGS |
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Repositório Institucional da UFRGS |
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http://www.lume.ufrgs.br/bitstream/10183/224903/2/000819551.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/224903/1/000819551.pdf |
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Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
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