Vulcont: A Recommender System based on Contexts History Ontology
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) |
Texto Completo: | http://www.repositorio.jesuita.org.br/handle/UNISINOS/6352 |
Resumo: | The use of recommender systems is already widespread. Everyday people are exposed to different items’ offering that infer their interest and anticipate decisions. The context information (such as location, goals, and entities around a context) plays a key role in the recommendation’s accuracy. Extending contexts snapshots into contexts histories enables that information to be exploit. It is possible to identify context’s sequences, similar contexts histories and even predict future contexts. In this work we present Vulcont, a recommender system based on a contexts history ontology. Vulcont merges the benefits of ontology reasoning with contexts histories in order to measure contexts history similarity, based on semantic and ontology’s properties provided by context’s domain. Vulcont considers synonymous and classes’ relations to measure similarity. After that, a collaborative filtering approach identifies sequences’ frequency to identify potential items for recommendation. We evaluated and discussed the Vulcont’s recommendation in four scenarios in an offline experiment, which presents Vulcont’s recommendation power, due the exploit of semantic value of contexts history. |
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2017-06-14T16:52:48Z2017-06-14T16:52:48Z2017-03-16Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-14T16:52:48Z No. of bitstreams: 1 Ismael Messias Gomes Cardoso_.pdf: 1842376 bytes, checksum: 60eed8afd2b70fbe501d63c0e1b81c39 (MD5)Made available in DSpace on 2017-06-14T16:52:48Z (GMT). No. of bitstreams: 1 Ismael Messias Gomes Cardoso_.pdf: 1842376 bytes, checksum: 60eed8afd2b70fbe501d63c0e1b81c39 (MD5) Previous issue date: 2017-03-16The use of recommender systems is already widespread. Everyday people are exposed to different items’ offering that infer their interest and anticipate decisions. The context information (such as location, goals, and entities around a context) plays a key role in the recommendation’s accuracy. Extending contexts snapshots into contexts histories enables that information to be exploit. It is possible to identify context’s sequences, similar contexts histories and even predict future contexts. In this work we present Vulcont, a recommender system based on a contexts history ontology. Vulcont merges the benefits of ontology reasoning with contexts histories in order to measure contexts history similarity, based on semantic and ontology’s properties provided by context’s domain. Vulcont considers synonymous and classes’ relations to measure similarity. After that, a collaborative filtering approach identifies sequences’ frequency to identify potential items for recommendation. We evaluated and discussed the Vulcont’s recommendation in four scenarios in an offline experiment, which presents Vulcont’s recommendation power, due the exploit of semantic value of contexts history.O uso de sistemas de recomendação já é amplamente difundido. Diariamente pessoas são expostas a ofertas de itens que inferem seus interesses e antecipam decisões. As informações de contexto (como localização, objetivos, e entidades que cercam um contexto) tem um papel chave na acurácia da recomendação. Ampliando o uso de contextos para histórico de contextos, essa informação pode ser explorada ainda mais. É possível identificar sequências de contextos, similaridade entre histórico de contextos, e até prever contextos futuros. Neste trabalho é apresentado o Vulcont, um sistema de recomendação baseado numa ontologia de histórico de contextos. Vulcont une os benefícios do raciocínio da ontologia com o uso de histórico de contextos para quantificar a similaridade entre histórico de contextos, com base na semântica e outras propriedades da ontologia definidas pelo domínio do contexto. Vulcont considera sinônimos e relações de classes para calcular a similaridade. Por seguinte, um filtro colaborativo identifica a frequência de sequências para estimar items em potencial de recomendação. As recomendações do Vulcont foram avaliadas e discutidas em quatro cenários num experimento offline. O experimento apresentou o poder de recomendação do Vulcont, que é devido a exploração do valor semântico de histórico de contextos.UNISINOS - Universidade do Vale do Rio dos SinosCardoso, Ismael Messias Gomeshttp://lattes.cnpq.br/5533293195733527http://lattes.cnpq.br/6754464380129137Barbosa, Jorge Luis VictóriaUniversidade do Vale do Rio dos SinosPrograma de Pós-Graduação em Computação AplicadaUnisinosBrasilEscola PolitécnicaVulcont: A Recommender System based on Contexts History OntologyACCNPQ::Ciências Exatas e da Terra::Ciência da ComputaçãoSistemas de recomendaçãoOntologiaHistórico de contextosFiltragem colaborativaRecommender systemsOntologyContexts historyCollaborative filteringinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://www.repositorio.jesuita.org.br/handle/UNISINOS/6352info:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos)instname:Universidade do Vale do Rio dos Sinos (UNISINOS)instacron:UNISINOSORIGINALIsmael Messias Gomes Cardoso_.pdfIsmael Messias Gomes Cardoso_.pdfapplication/pdf1842376http://repositorio.jesuita.org.br/bitstream/UNISINOS/6352/1/Ismael+Messias+Gomes+Cardoso_.pdf60eed8afd2b70fbe501d63c0e1b81c39MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82175http://repositorio.jesuita.org.br/bitstream/UNISINOS/6352/2/license.txt320e21f23402402ac4988605e1edd177MD52UNISINOS/63522017-06-14 13:54:45.579oai:www.repositorio.jesuita.org.br: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 Digital de Teses e Dissertaçõeshttp://www.repositorio.jesuita.org.br/oai/requestopendoar:2017-06-14T16:54:45Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) - Universidade do Vale do Rio dos Sinos (UNISINOS)false |
dc.title.pt_BR.fl_str_mv |
Vulcont: A Recommender System based on Contexts History Ontology |
title |
Vulcont: A Recommender System based on Contexts History Ontology |
spellingShingle |
Vulcont: A Recommender System based on Contexts History Ontology Cardoso, Ismael Messias Gomes ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação Sistemas de recomendação Ontologia Histórico de contextos Filtragem colaborativa Recommender systems Ontology Contexts history Collaborative filtering |
title_short |
Vulcont: A Recommender System based on Contexts History Ontology |
title_full |
Vulcont: A Recommender System based on Contexts History Ontology |
title_fullStr |
Vulcont: A Recommender System based on Contexts History Ontology |
title_full_unstemmed |
Vulcont: A Recommender System based on Contexts History Ontology |
title_sort |
Vulcont: A Recommender System based on Contexts History Ontology |
author |
Cardoso, Ismael Messias Gomes |
author_facet |
Cardoso, Ismael Messias Gomes |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/5533293195733527 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/6754464380129137 |
dc.contributor.author.fl_str_mv |
Cardoso, Ismael Messias Gomes |
dc.contributor.advisor1.fl_str_mv |
Barbosa, Jorge Luis Victória |
contributor_str_mv |
Barbosa, Jorge Luis Victória |
dc.subject.cnpq.fl_str_mv |
ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação |
topic |
ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação Sistemas de recomendação Ontologia Histórico de contextos Filtragem colaborativa Recommender systems Ontology Contexts history Collaborative filtering |
dc.subject.por.fl_str_mv |
Sistemas de recomendação Ontologia Histórico de contextos Filtragem colaborativa |
dc.subject.eng.fl_str_mv |
Recommender systems Ontology Contexts history Collaborative filtering |
description |
The use of recommender systems is already widespread. Everyday people are exposed to different items’ offering that infer their interest and anticipate decisions. The context information (such as location, goals, and entities around a context) plays a key role in the recommendation’s accuracy. Extending contexts snapshots into contexts histories enables that information to be exploit. It is possible to identify context’s sequences, similar contexts histories and even predict future contexts. In this work we present Vulcont, a recommender system based on a contexts history ontology. Vulcont merges the benefits of ontology reasoning with contexts histories in order to measure contexts history similarity, based on semantic and ontology’s properties provided by context’s domain. Vulcont considers synonymous and classes’ relations to measure similarity. After that, a collaborative filtering approach identifies sequences’ frequency to identify potential items for recommendation. We evaluated and discussed the Vulcont’s recommendation in four scenarios in an offline experiment, which presents Vulcont’s recommendation power, due the exploit of semantic value of contexts history. |
publishDate |
2017 |
dc.date.accessioned.fl_str_mv |
2017-06-14T16:52:48Z |
dc.date.available.fl_str_mv |
2017-06-14T16:52:48Z |
dc.date.issued.fl_str_mv |
2017-03-16 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.repositorio.jesuita.org.br/handle/UNISINOS/6352 |
url |
http://www.repositorio.jesuita.org.br/handle/UNISINOS/6352 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade do Vale do Rio dos Sinos |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Computação Aplicada |
dc.publisher.initials.fl_str_mv |
Unisinos |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Escola Politécnica |
publisher.none.fl_str_mv |
Universidade do Vale do Rio dos Sinos |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) instname:Universidade do Vale do Rio dos Sinos (UNISINOS) instacron:UNISINOS |
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Universidade do Vale do Rio dos Sinos (UNISINOS) |
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UNISINOS |
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UNISINOS |
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Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) |
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Repositório Institucional da UNISINOS (RBDU Repositório Digital da Biblioteca da Unisinos) |
bitstream.url.fl_str_mv |
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