Exploiting multimodal interactions in recommender systems with ensemble algorithms (2016)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.is.2015.09.007
- Subjects: WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- Source:
- Título: Information Systems
- ISSN: 0306-4379
- Volume/Número/Paginação/Ano: v. 56, p. 120-132, Mar. 2016
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
COSTA, Arthur F. da e MANZATO, Marcelo Garcia. Exploiting multimodal interactions in recommender systems with ensemble algorithms. Information Systems, v. 56, p. 120-132, 2016Tradução . . Disponível em: https://doi.org/10.1016/j.is.2015.09.007. Acesso em: 21 fev. 2026. -
APA
Costa, A. F. da, & Manzato, M. G. (2016). Exploiting multimodal interactions in recommender systems with ensemble algorithms. Information Systems, 56, 120-132. doi:10.1016/j.is.2015.09.007 -
NLM
Costa AF da, Manzato MG. Exploiting multimodal interactions in recommender systems with ensemble algorithms [Internet]. Information Systems. 2016 ; 56 120-132.[citado 2026 fev. 21 ] Available from: https://doi.org/10.1016/j.is.2015.09.007 -
Vancouver
Costa AF da, Manzato MG. Exploiting multimodal interactions in recommender systems with ensemble algorithms [Internet]. Information Systems. 2016 ; 56 120-132.[citado 2026 fev. 21 ] Available from: https://doi.org/10.1016/j.is.2015.09.007 - Metadata in movies recommendation: a comparison among different approaches
- gSVD++: supporting implicit feedback on recommender systems with metadata awareness
- A collaborative filtering approach based on user's reviews
- Multimodal interactions in recommender systems: an ensembling approach
- Exploiting feature extraction techniques on users' reviews for movies recommendation
- Exploiting item representations for soft clustering recommendation
- Combining different metadata views for better recommendation accuracy
- CoBaR: confidence-based recommender
- Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization
- Personalized ranking of movies: evaluating different metadata types and recommendation strategies
Informações sobre o DOI: 10.1016/j.is.2015.09.007 (Fonte: oaDOI API)
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