Multimodal interactions in recommender systems: an ensembling approach (2014)
- Authors:
- USP affiliated author: MANZATO, MARCELO GARCIA - ICMC
- School: ICMC
- DOI: 10.1109/BRACIS.2014.23
- Subjects: WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Place of publication: Los Alamitos
- Date published: 2014
- ISBN: 9781479956180
- Source:
- Título do periódico: Proceedings
- Conference title: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
COSTA, Arthur F. da e MANZATO, Marcelo Garcia. Multimodal interactions in recommender systems: an ensembling approach. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: http://dx.doi.org/10.1109/BRACIS.2014.23. Acesso em: 28 jun. 2022. -
APA
Costa, A. F. da, & Manzato, M. G. (2014). Multimodal interactions in recommender systems: an ensembling approach. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.23 -
NLM
Costa AF da, Manzato MG. Multimodal interactions in recommender systems: an ensembling approach [Internet]. Proceedings. 2014 ;[citado 2022 jun. 28 ] Available from: http://dx.doi.org/10.1109/BRACIS.2014.23 -
Vancouver
Costa AF da, Manzato MG. Multimodal interactions in recommender systems: an ensembling approach [Internet]. Proceedings. 2014 ;[citado 2022 jun. 28 ] Available from: http://dx.doi.org/10.1109/BRACIS.2014.23 - Uma arquitetura de personalização de conteúdo baseada em anotações do usuário
- Incorporating semantic item representations to soften the cold start problem
- CoBaR: confidence-based recommender
- Evaluating the combination of multiple metadata types in movies recommendation
- Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
- Semantic organization of user's reviews applied in recommender systems
- Exploiting item representations for soft clustering recommendation
- Combining different metadata views for better recommendation accuracy
- Exploiting feature extraction techniques on users' reviews for movies recommendation
- Exploiting personalized calibration and metrics for fairness recommendation
Informações sobre o DOI: 10.1109/BRACIS.2014.23 (Fonte: oaDOI API)
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