A sentiment-based item description approach for kNN collaborative filtering (2015)
- Authors:
- Autor USP: MANZATO, MARCELO GARCIA - ICMC
- Unidade: ICMC
- DOI: 10.1145/2695664.2695747
- Subjects: WORLD WIDE WEB; SISTEMAS MULTIMÍDIA
- Language: Inglês
- Imprenta:
- ISBN: 9781450331968
- Source:
- Título: Proceedings
- Conference titles: Symposium on Applied Computing - SAC
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
D'ADDIO, Rafael M e MANZATO, Marcelo Garcia. A sentiment-based item description approach for kNN collaborative filtering. 2015, Anais.. New York: ACM, 2015. Disponível em: https://doi.org/10.1145/2695664.2695747. Acesso em: 20 fev. 2026. -
APA
D'Addio, R. M., & Manzato, M. G. (2015). A sentiment-based item description approach for kNN collaborative filtering. In Proceedings. New York: ACM. doi:10.1145/2695664.2695747 -
NLM
D'Addio RM, Manzato MG. A sentiment-based item description approach for kNN collaborative filtering [Internet]. Proceedings. 2015 ;[citado 2026 fev. 20 ] Available from: https://doi.org/10.1145/2695664.2695747 -
Vancouver
D'Addio RM, Manzato MG. A sentiment-based item description approach for kNN collaborative filtering [Internet]. Proceedings. 2015 ;[citado 2026 fev. 20 ] Available from: https://doi.org/10.1145/2695664.2695747 - 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: 10.1145/2695664.2695747 (Fonte: oaDOI API)
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