Forgetting mechanisms for scalable collaborative filtering
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/3342 http://dx.doi.org/10.1007/s13173-012-0077-3 |
Resumo: | Collaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms - sliding windows and fading factors - in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms. |
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Forgetting mechanisms for scalable collaborative filteringCollaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms - sliding windows and fading factors - in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms.2017-11-17T11:59:40Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/3342http://dx.doi.org/10.1007/s13173-012-0077-3engAlípio JorgeJoão Marques Silvainfo: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:RCAAP2023-05-15T10:20:00Zoai:repositorio.inesctec.pt:123456789/3342Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:32.864073Repositório 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 |
Forgetting mechanisms for scalable collaborative filtering |
title |
Forgetting mechanisms for scalable collaborative filtering |
spellingShingle |
Forgetting mechanisms for scalable collaborative filtering Alípio Jorge |
title_short |
Forgetting mechanisms for scalable collaborative filtering |
title_full |
Forgetting mechanisms for scalable collaborative filtering |
title_fullStr |
Forgetting mechanisms for scalable collaborative filtering |
title_full_unstemmed |
Forgetting mechanisms for scalable collaborative filtering |
title_sort |
Forgetting mechanisms for scalable collaborative filtering |
author |
Alípio Jorge |
author_facet |
Alípio Jorge João Marques Silva |
author_role |
author |
author2 |
João Marques Silva |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Alípio Jorge João Marques Silva |
description |
Collaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms - sliding windows and fading factors - in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01T00:00:00Z 2012 2017-11-17T11:59:40Z |
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://repositorio.inesctec.pt/handle/123456789/3342 http://dx.doi.org/10.1007/s13173-012-0077-3 |
url |
http://repositorio.inesctec.pt/handle/123456789/3342 http://dx.doi.org/10.1007/s13173-012-0077-3 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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 |
institution |
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 |
repository.mail.fl_str_mv |
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1799131601391583232 |