Simultaneous co-clustering and learning to address the cold start problem in recommender systems (2015)
- Autores:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.knosys.2015.02.016
- Assuntos: INTELIGÊNCIA ARTIFICIAL; APRENDIZADO COMPUTACIONAL; MINERAÇÃO DE DADOS
- Idioma: Inglês
- Imprenta:
- Fonte:
- Título do periódico: Knowledge-Based Systems
- ISSN: 0950-7051
- Volume/Número/Paginação/Ano: v. 82, p. 11-19, jul. 2015
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
PEREIRA, Andre Luiz Vizine e HRUSCHKA, Eduardo Raul. Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, v. 82, p. 11-19, 2015Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2015.02.016. Acesso em: 23 abr. 2024. -
APA
Pereira, A. L. V., & Hruschka, E. R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82, 11-19. doi:10.1016/j.knosys.2015.02.016 -
NLM
Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016 -
Vancouver
Pereira ALV, Hruschka ER. Simultaneous co-clustering and learning to address the cold start problem in recommender systems [Internet]. Knowledge-Based Systems. 2015 ; 82 11-19.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.knosys.2015.02.016 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
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Informações sobre o DOI: 10.1016/j.knosys.2015.02.016 (Fonte: oaDOI API)
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