Evaluating the combination of multiple metadata types in movies recommendation (2014)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1109/BRACIS.2014.21
- Subjects: WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Publisher place: Los Alamitos
- Date published: 2014
- ISBN: 9781479956180
- Source:
- Título do periódico: Proceedings
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
BELTRÃO, Renato D et al. Evaluating the combination of multiple metadata types in movies recommendation. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.21. Acesso em: 29 mar. 2024. -
APA
Beltrão, R. D., Manzato, M. G., Cabral, B. S., & Durão, F. (2014). Evaluating the combination of multiple metadata types in movies recommendation. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.21 -
NLM
Beltrão RD, Manzato MG, Cabral BS, Durão F. Evaluating the combination of multiple metadata types in movies recommendation [Internet]. Proceedings. 2014 ;[citado 2024 mar. 29 ] Available from: https://doi.org/10.1109/BRACIS.2014.21 -
Vancouver
Beltrão RD, Manzato MG, Cabral BS, Durão F. Evaluating the combination of multiple metadata types in movies recommendation [Internet]. Proceedings. 2014 ;[citado 2024 mar. 29 ] Available from: https://doi.org/10.1109/BRACIS.2014.21 - Incorporating semantic item representations to soften the cold start problem
- Uma arquitetura de personalização de conteúdo baseada em anotações do usuário
- gSVD++: supporting implicit feedback on recommender systems with metadata awareness
- Metadata in movies recommendation: a comparison among different approaches
- A collaborative filtering approach based on user's reviews
- Multimodal interactions in recommender systems: an ensembling approach
- A sentiment-based item description approach for kNN collaborative filtering
- Similarity-based matrix factorization for item cold-start in recommender systems
- Combining multiple metadata types in movies recommendation using ensemble algorithms
- Exploiting multimodal interactions in recommender systems with ensemble algorithms
Informações sobre o DOI: 10.1109/BRACIS.2014.21 (Fonte: oaDOI API)
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