Filtragem Colaborativa Incremental para recomendações automáticas na Web
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10216/20592 |
Resumo: | The use of collaborative filtering recommenders on the Web is typically done in environments where data is constantly flowing and new customers and products are emerging. In this work, it is proposed an incremental version of item-based Collaborative Filtering for implicit binary ratings. It is compared with a non-incremental one, as well as with an incremental user-based approach. It is also study the use of techniques for working with sparse matrices on these algorithms. All the versions are implemented in R and are empirically evaluated on five different datasets with various number of users and/or items. It is observed that the measure of Recall used tend to improve when we continuously add information to the recommender model and that the time spent for recommendation does not degrade. Time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach. |
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Filtragem Colaborativa Incremental para recomendações automáticas na WebINFORMÁTICAPortoThe use of collaborative filtering recommenders on the Web is typically done in environments where data is constantly flowing and new customers and products are emerging. In this work, it is proposed an incremental version of item-based Collaborative Filtering for implicit binary ratings. It is compared with a non-incremental one, as well as with an incremental user-based approach. It is also study the use of techniques for working with sparse matrices on these algorithms. All the versions are implemented in R and are empirically evaluated on five different datasets with various number of users and/or items. It is observed that the measure of Recall used tend to improve when we continuously add information to the recommender model and that the time spent for recommendation does not degrade. Time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach.Faculdade de Economia da Universidade do PortoFEP20082009-05-12T00:00:00Z2009-05-12info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10216/20592porMiranda, Ana Catarina de Pinhoinfo: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:RCAAP2023-11-29T13:21:27Zoai:repositorio-aberto.up.pt:10216/20592Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:39:06.412210Repositório 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 |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
title |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
spellingShingle |
Filtragem Colaborativa Incremental para recomendações automáticas na Web Miranda, Ana Catarina de Pinho INFORMÁTICA Porto |
title_short |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
title_full |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
title_fullStr |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
title_full_unstemmed |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
title_sort |
Filtragem Colaborativa Incremental para recomendações automáticas na Web |
author |
Miranda, Ana Catarina de Pinho |
author_facet |
Miranda, Ana Catarina de Pinho |
author_role |
author |
dc.contributor.author.fl_str_mv |
Miranda, Ana Catarina de Pinho |
dc.subject.por.fl_str_mv |
INFORMÁTICA Porto |
topic |
INFORMÁTICA Porto |
description |
The use of collaborative filtering recommenders on the Web is typically done in environments where data is constantly flowing and new customers and products are emerging. In this work, it is proposed an incremental version of item-based Collaborative Filtering for implicit binary ratings. It is compared with a non-incremental one, as well as with an incremental user-based approach. It is also study the use of techniques for working with sparse matrices on these algorithms. All the versions are implemented in R and are empirically evaluated on five different datasets with various number of users and/or items. It is observed that the measure of Recall used tend to improve when we continuously add information to the recommender model and that the time spent for recommendation does not degrade. Time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008 2009-05-12T00:00:00Z 2009-05-12 |
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://hdl.handle.net/10216/20592 |
url |
http://hdl.handle.net/10216/20592 |
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 |
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application/pdf |
dc.publisher.none.fl_str_mv |
Faculdade de Economia da Universidade do Porto FEP |
publisher.none.fl_str_mv |
Faculdade de Economia da Universidade do Porto FEP |
dc.source.none.fl_str_mv |
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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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