Exploiting feature extraction techniques on users' reviews for movies recommendation (2017)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1186/s13173-017-0057-8
- Subjects: MULTIMÍDIA INTERATIVA; RECONHECIMENTO DE TEXTO
- Keywords: Recommender systems; Item representation; Feature extraction; Sentiment analysis
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2017
- Source:
- Título: Journal of the Brazilian Computer Society
- ISSN: 1678-4804
- Volume/Número/Paginação/Ano: v. 23, p. 1-16, 2017
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
D'ADDIO, Rafael M e DOMINGUES, Marcos A e MANZATO, Marcelo Garcia. Exploiting feature extraction techniques on users' reviews for movies recommendation. Journal of the Brazilian Computer Society, v. 23, p. 1-16, 2017Tradução . . Disponível em: https://doi.org/10.1186/s13173-017-0057-8. Acesso em: 28 fev. 2026. -
APA
D'Addio, R. M., Domingues, M. A., & Manzato, M. G. (2017). Exploiting feature extraction techniques on users' reviews for movies recommendation. Journal of the Brazilian Computer Society, 23, 1-16. doi:10.1186/s13173-017-0057-8 -
NLM
D'Addio RM, Domingues MA, Manzato MG. Exploiting feature extraction techniques on users' reviews for movies recommendation [Internet]. Journal of the Brazilian Computer Society. 2017 ; 23 1-16.[citado 2026 fev. 28 ] Available from: https://doi.org/10.1186/s13173-017-0057-8 -
Vancouver
D'Addio RM, Domingues MA, Manzato MG. Exploiting feature extraction techniques on users' reviews for movies recommendation [Internet]. Journal of the Brazilian Computer Society. 2017 ; 23 1-16.[citado 2026 fev. 28 ] Available from: https://doi.org/10.1186/s13173-017-0057-8 - 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 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
- Evaluating multiple user interactions for ranking personalization using ensemble methods
Informações sobre o DOI: 10.1186/s13173-017-0057-8 (Fonte: oaDOI API)
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