Exploiting text mining techniques for contextual recommendations (2014)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; REZENDE, SOLANGE OLIVEIRA - ICMC
- Unidade: ICMC
- DOI: 10.1109/WI-IAT.2014.100
- Subjects: WORLD WIDE WEB; SISTEMAS MULTIMÍDIA; INTELIGÊNCIA ARTIFICIAL; MINERAÇÃO DE DADOS
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Publisher place: Los Alamitos
- Date published: 2014
- ISBN: 9781479941438
- Source:
- Título: Proceedings
- Conference titles: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies - WI-IAT
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
DOMINGUES, Marcos Aurélio et al. Exploiting text mining techniques for contextual recommendations. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/WI-IAT.2014.100. Acesso em: 12 out. 2024. -
APA
Domingues, M. A., Sundermann, C. V., Manzato, M. G., Marcacini, R. M., & Rezende, S. O. (2014). Exploiting text mining techniques for contextual recommendations. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/WI-IAT.2014.100 -
NLM
Domingues MA, Sundermann CV, Manzato MG, Marcacini RM, Rezende SO. Exploiting text mining techniques for contextual recommendations [Internet]. Proceedings. 2014 ;[citado 2024 out. 12 ] Available from: https://doi.org/10.1109/WI-IAT.2014.100 -
Vancouver
Domingues MA, Sundermann CV, Manzato MG, Marcacini RM, Rezende SO. Exploiting text mining techniques for contextual recommendations [Internet]. Proceedings. 2014 ;[citado 2024 out. 12 ] Available from: https://doi.org/10.1109/WI-IAT.2014.100 - Improving personalized ranking in recommender systems with multimodal interactions
- Optimizing personalized ranking in recommender systems with metadata awareness
- Using contextual information from topic hierarchies to improve context-aware recommender systems
- Improving personalized ranking in recommender systems with topic hierarchies and implicit feedback
- Generating recommendations based on robust term extraction from users' reviews
- Mining unstructured content for recommender systems: an ensemble approach
- Applying multi-view based metadata in personalized ranking for recommender systems
- Uma arquitetura de personalização de conteúdo baseada em anotações do usuário
- Incorporating semantic item representations to soften the cold start problem
- Evaluating the combination of multiple metadata types in movies recommendation
Informações sobre o DOI: 10.1109/WI-IAT.2014.100 (Fonte: oaDOI API)
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