Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking (2016)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1145/2976796.2976852
- Subjects: MULTIMÍDIA INTERATIVA; SISTEMAS DE INFORMAÇÃO; RECONHECIMENTO DE PADRÕES
- Keywords: Recommender Systems; Collaborative Filtering; Data Clustering
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Brazilian Symposium on Multimedia and the Web - WebMedia
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
COSTA, Arthur F. da e MANZATO, Marcelo Garcia e CAMPELLO, Ricardo José Gabrielli Barreto. Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking. 2016, Anais.. New York: ACM, 2016. Disponível em: https://doi.org/10.1145/2976796.2976852. Acesso em: 21 fev. 2026. -
APA
Costa, A. F. da, Manzato, M. G., & Campello, R. J. G. B. (2016). Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking. In Proceedings. New York: ACM. doi:10.1145/2976796.2976852 -
NLM
Costa AF da, Manzato MG, Campello RJGB. Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking [Internet]. Proceedings. 2016 ;[citado 2026 fev. 21 ] Available from: https://doi.org/10.1145/2976796.2976852 -
Vancouver
Costa AF da, Manzato MG, Campello RJGB. Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking [Internet]. Proceedings. 2016 ;[citado 2026 fev. 21 ] Available from: https://doi.org/10.1145/2976796.2976852 - CoRec: a co-training approach for recommender systems
- Exploiting different users' interactions for profiles enrichment in recommender systems
- Ensemble clustering approaches applied in group-based collaborative filtering supported by multiple users' feedback
- Case recommender: a flexible and extensible Python framework for recommender systems
- Boosting collaborative filtering with an ensemble of co-trained recommenders
- 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
Informações sobre o DOI: 10.1145/2976796.2976852 (Fonte: oaDOI API)
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