TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services

Detalhes bibliográficos
Autor(a) principal: Soares, Márcio
Data de Publicação: 2014
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/10400.22/5279
Resumo: he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.
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spelling TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand ServicesCollaborative filteringContent filteringRecommendation systemsTV-Anytimehe expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.FCTRepositório Científico do Instituto Politécnico do PortoSoares, MárcioViana, Paula2015-01-05T12:58:59Z2014-02-012014-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/5279eng1582-744510.4316/AECE.1014.01018info: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:RCAAP2023-03-13T12:45:23Zoai:recipp.ipp.pt:10400.22/5279Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:26:00.599731Repositório 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 TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
title TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
spellingShingle TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
Soares, Márcio
Collaborative filtering
Content filtering
Recommendation systems
TV-Anytime
title_short TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
title_full TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
title_fullStr TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
title_full_unstemmed TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
title_sort TV Recommendation and Personalization Systems: Integrating Broadcast and Video On-demand Services
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 Collaborative filtering
Content filtering
Recommendation systems
TV-Anytime
topic Collaborative filtering
Content filtering
Recommendation systems
TV-Anytime
description he expansion of Digital Television and the convergence between conventional broadcasting and television over IP contributed to the gradual increase of the number of available channels and on demand video content. Moreover, the dissemination of the use of mobile devices like laptops, smartphones and tablets on everyday activities resulted in a shift of the traditional television viewing paradigm from the couch to everywhere, anytime from any device. Although this new scenario enables a great improvement in viewing experiences, it also brings new challenges given the overload of information that the viewer faces. Recommendation systems stand out as a possible solution to help a watcher on the selection of the content that best fits his/her preferences. This paper describes a web based system that helps the user navigating on broadcasted and online television content by implementing recommendations based on collaborative and content based filtering. The algorithms developed estimate the similarity between items and users and predict the rating that a user would assign to a particular item (television program, movie, etc.). To enable interoperability between different systems, programs characteristics (title, genre, actors, etc.) are stored according to the TV-Anytime standard. The set of recommendations produced are presented through a Web Application that allows the user to interact with the system based on the obtained recommendations.
publishDate 2014
dc.date.none.fl_str_mv 2014-02-01
2014-02-01T00:00:00Z
2015-01-05T12:58:59Z
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dc.relation.none.fl_str_mv 1582-7445
10.4316/AECE.1014.01018
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