Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback (2017)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- Subjects: RECONHECIMENTO DE PADRÕES; RECUPERAÇÃO DA INFORMAÇÃO
- Keywords: Data clustering; Ensemble; Feedback; Recommender systems
- Language: Inglês
- Imprenta:
- Publisher place: Porto Alegre
- Date published: 2017
- Source:
- Título: Journal of Information and Data Management - JIDM
- ISSN: 2178-7107
- Volume/Número/Paginação/Ano: v. 8, n. 3, p. 180-196, Dec. 2017
-
ABNT
COSTA, Arthur F. da e MANZATO, Marcelo Garcia e CAMPELLO, Ricardo José Gabrielli Barreto. Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback. Journal of Information and Data Management - JIDM, v. 8, n. 3, p. 180-196, 2017Tradução . . Disponível em: https://seer.ufmg.br/index.php/jidm/article/view/4560. Acesso em: 22 fev. 2026. -
APA
Costa, A. F. da, Manzato, M. G., & Campello, R. J. G. B. (2017). Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback. Journal of Information and Data Management - JIDM, 8( 3), 180-196. Recuperado de https://seer.ufmg.br/index.php/jidm/article/view/4560 -
NLM
Costa AF da, Manzato MG, Campello RJGB. Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback [Internet]. Journal of Information and Data Management - JIDM. 2017 ; 8( 3): 180-196.[citado 2026 fev. 22 ] Available from: https://seer.ufmg.br/index.php/jidm/article/view/4560 -
Vancouver
Costa AF da, Manzato MG, Campello RJGB. Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback [Internet]. Journal of Information and Data Management - JIDM. 2017 ; 8( 3): 180-196.[citado 2026 fev. 22 ] Available from: https://seer.ufmg.br/index.php/jidm/article/view/4560 - CoRec: a co-training approach for recommender systems
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- Boosting collaborative filtering with an ensemble of co-trained recommenders
- Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking
- Similarity measures for comparing biclusterings
- Density-based clustering validation
- Relative validity criteria for community mining algorithms
- Active learning strategies for semi-supervised DBSCAN
- On the evaluation of outlier detection and one-class classification methods
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