Recomendação semântica de conteúdo em ambientes de convergência digital

• Ano de defesa: 2013
• Autor(a) principal: Vieira, Priscilla Kelly Machado
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: TV Digital interativa; Sistemas de Recomendação; Ginga; Web Semântica, Mineração de Dados.; Interactive Digital TV; Recommender Systems TV Digital Interativa; Ginga, Semantic Web, Data Mining.; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/7826

The emerging scenario of interactive Digital TV (iDTV) is promoting the increase of interactivity in the communication process and also in audiovisual production, thus rising the number of channels and resources available to the user. This reality makes the task of finding the desired content becoming a costly and possibly ineffective action. The incorporation of recommender systems in the iDTV environment is emerging as a possible solution to this problem. This work aims to propose a hybrid approach to content recommendation in iDTV, based on data mining techniques, integrated the concepts of the Semantic Web, allowing structuring and standardization of data and consequent possibility of sharing information, providing semantics and automated reasoning. For the proposed service is considered the Brazilian Digital TV System and the middleware Ginga. A prototype has been developed and carried out experiments with NetFlix database using the measuring accuracy for evaluation. There was obtained an average accuracy of 30% using only mining technique. Including semantic rules obtained average accuracy of 35%.