The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation

Detalhes bibliográficos
Autor(a) principal: Soares, Márcio
Data de Publicação: 2017
Outros Autores: Viana, Paula
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/10777
Resumo: In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.
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spelling The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid RecommendationUser profilingHybrid recommendationMovie metadataSemantic knowledgeIn movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.Springer Publishing CompanyRepositório Científico do Instituto Politécnico do PortoSoares, MárcioViana, Paula2018-01-16T10:46:10Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/10777eng10.1007/978-3-319-56535-4_33info: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:RCAAP2023-03-13T12:52:47Zoai:recipp.ipp.pt:10400.22/10777Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:31:12.559744Repositório 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 The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
title The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
spellingShingle The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
Soares, Márcio
User profiling
Hybrid recommendation
Movie metadata
Semantic knowledge
title_short The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
title_full The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
title_fullStr The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
title_full_unstemmed The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
title_sort The Semantics of Movie Metadata: Enhancing User Profiling for Hybrid Recommendation
author Soares, Márcio
author_facet Soares, Márcio
Viana, Paula
author_role author
author2 Viana, Paula
author2_role author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Soares, Márcio
Viana, Paula
dc.subject.por.fl_str_mv User profiling
Hybrid recommendation
Movie metadata
Semantic knowledge
topic User profiling
Hybrid recommendation
Movie metadata
Semantic knowledge
description In movie/TV collaborative recommendation approaches, ratings users gave to already visited content are often used as the only input to build profiles. However, users might have rated equally the same movie but due to different reasons: either because of its genre, the crew or the director. In such cases, this rating is insufficient to represent in detail users’ preferences and it is wrong to conclude that they share similar tastes. The work presented in this paper tries to solve this ambiguity by exploiting hidden semantics in metadata elements. The influence of each of the standard description elements (actors, directors and genre) in representing user’s preferences is analyzed. Simulations were conducted using Movielens and Netflix datasets and different evaluation metrics were considered. The results demonstrate that the implemented approach yields significant advantages both in terms of improving performance, as well as in dealing with common limitations of standard collaborative algorithm.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2018-01-16T10:46:10Z
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/10400.22/10777
url http://hdl.handle.net/10400.22/10777
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1007/978-3-319-56535-4_33
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.publisher.none.fl_str_mv Springer Publishing Company
publisher.none.fl_str_mv Springer Publishing Company
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
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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