Discovering latent factors from movies genres for enhanced recommendation (2012)
- Autor:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1145/2365952.2366006
- Subjects: MULTIMÍDIA INTERATIVA; WORLD WIDE WEB
- Language: Inglês
- Imprenta:
- ISBN: 978145031270-7
- Source:
- Título: Proceedings
- Conference titles: ACM Conference on Recommender Systems - RecSys
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
MANZATO, Marcelo Garcia. Discovering latent factors from movies genres for enhanced recommendation. 2012, Anais.. New York: ACM, 2012. Disponível em: https://doi.org/10.1145/2365952.2366006. Acesso em: 20 mar. 2026. -
APA
Manzato, M. G. (2012). Discovering latent factors from movies genres for enhanced recommendation. In Proceedings. New York: ACM. doi:10.1145/2365952.2366006 -
NLM
Manzato MG. Discovering latent factors from movies genres for enhanced recommendation [Internet]. Proceedings. 2012 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1145/2365952.2366006 -
Vancouver
Manzato MG. Discovering latent factors from movies genres for enhanced recommendation [Internet]. Proceedings. 2012 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1145/2365952.2366006 - 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
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