A collaborative filtering approach based on user's reviews (2014)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1109/BRACIS.2014.45
- Subjects: RECUPERAÇÃO DA INFORMAÇÃO; WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- Publisher: Conference Publishing Services
- Publisher place: Los Alamitos
- Date published: 2014
- ISBN: 9781479956180
- Source:
- Título: Proceedings
- Conference titles: Brazilian Conference on Intelligent Systems - BRACIS
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
D'ADDIO, Rafael Martins e MANZATO, Marcelo Garcia. A collaborative filtering approach based on user's reviews. 2014, Anais.. Los Alamitos: Conference Publishing Services, 2014. Disponível em: https://doi.org/10.1109/BRACIS.2014.45. Acesso em: 10 fev. 2026. -
APA
D'Addio, R. M., & Manzato, M. G. (2014). A collaborative filtering approach based on user's reviews. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2014.45 -
NLM
D'Addio RM, Manzato MG. A collaborative filtering approach based on user's reviews [Internet]. Proceedings. 2014 ;[citado 2026 fev. 10 ] Available from: https://doi.org/10.1109/BRACIS.2014.45 -
Vancouver
D'Addio RM, Manzato MG. A collaborative filtering approach based on user's reviews [Internet]. Proceedings. 2014 ;[citado 2026 fev. 10 ] Available from: https://doi.org/10.1109/BRACIS.2014.45 - Metadata in movies recommendation: a comparison among different approaches
- gSVD++: supporting implicit feedback on recommender systems with metadata awareness
- 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
- Evaluating multiple user interactions for ranking personalization using ensemble methods
Informações sobre o DOI: 10.1109/BRACIS.2014.45 (Fonte: oaDOI API)
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