Recommending media content based on machine learning methods

Detalhes bibliográficos
Autor(a) principal: Dias, Pedro Ricardo Gomes
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/6581
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Informática
id RCAP_769e6ff9c0bf7a573cac0e6b3451658f
oai_identifier_str oai:run.unl.pt:10362/6581
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Recommending media content based on machine learning methodsRecommender systemsCollaborative filteringMatrix factorizationGroupbased recommendationsInteractive TVDissertação para obtenção do Grau de Mestre em Engenharia InformáticaInformation is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and valuable services. The goal of this thesis is to develop a recommender system based on matrix factorization methods, capable of providing meaningful and personalized product recommendations to individual users and groups of users, by taking into account users’ rating patterns and biased tendencies, as well as their fluctuations throughout time.Faculdade de Ciências e TecnologiaMagalhães, JoãoRUNDias, Pedro Ricardo Gomes2011-12-28T16:12:27Z20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/6581enginfo: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:RCAAP2024-03-11T03:37:41Zoai:run.unl.pt:10362/6581Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:16:53.063340Repositório 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 Recommending media content based on machine learning methods
title Recommending media content based on machine learning methods
spellingShingle Recommending media content based on machine learning methods
Dias, Pedro Ricardo Gomes
Recommender systems
Collaborative filtering
Matrix factorization
Groupbased recommendations
Interactive TV
title_short Recommending media content based on machine learning methods
title_full Recommending media content based on machine learning methods
title_fullStr Recommending media content based on machine learning methods
title_full_unstemmed Recommending media content based on machine learning methods
title_sort Recommending media content based on machine learning methods
author Dias, Pedro Ricardo Gomes
author_facet Dias, Pedro Ricardo Gomes
author_role author
dc.contributor.none.fl_str_mv Magalhães, João
RUN
dc.contributor.author.fl_str_mv Dias, Pedro Ricardo Gomes
dc.subject.por.fl_str_mv Recommender systems
Collaborative filtering
Matrix factorization
Groupbased recommendations
Interactive TV
topic Recommender systems
Collaborative filtering
Matrix factorization
Groupbased recommendations
Interactive TV
description Dissertação para obtenção do Grau de Mestre em Engenharia Informática
publishDate 2011
dc.date.none.fl_str_mv 2011-12-28T16:12:27Z
2011
2011-01-01T00:00:00Z
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/10362/6581
url http://hdl.handle.net/10362/6581
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.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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
repository.mail.fl_str_mv
_version_ 1799137817560875008

Registros relacionados