Boosting collaborative filtering with an ensemble of co-trained recommenders (2019)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.eswa.2018.08.020
- Subjects: APRENDIZADO COMPUTACIONAL; SISTEMAS DE INFORMAÇÃO; RECONHECIMENTO DE PADRÕES
- Keywords: Co-training; Ensembles; Recommender systems; Semi-supervised learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Expert Systems with Applications
- ISSN: 0957-4174
- Volume/Número/Paginação/Ano: v. 115, p. 427-441, Jan. 2019
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
COSTA, Arthur F. da e MANZATO, Marcelo Garcia e CAMPELLO, Ricardo José Gabrielli Barreto. Boosting collaborative filtering with an ensemble of co-trained recommenders. Expert Systems with Applications, v. 115, n. Ja 2019, p. 427-441, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2018.08.020. Acesso em: 17 out. 2024. -
APA
Costa, A. F. da, Manzato, M. G., & Campello, R. J. G. B. (2019). Boosting collaborative filtering with an ensemble of co-trained recommenders. Expert Systems with Applications, 115( Ja 2019), 427-441. doi:10.1016/j.eswa.2018.08.020 -
NLM
Costa AF da, Manzato MG, Campello RJGB. Boosting collaborative filtering with an ensemble of co-trained recommenders [Internet]. Expert Systems with Applications. 2019 ; 115( Ja 2019): 427-441.[citado 2024 out. 17 ] Available from: https://doi.org/10.1016/j.eswa.2018.08.020 -
Vancouver
Costa AF da, Manzato MG, Campello RJGB. Boosting collaborative filtering with an ensemble of co-trained recommenders [Internet]. Expert Systems with Applications. 2019 ; 115( Ja 2019): 427-441.[citado 2024 out. 17 ] Available from: https://doi.org/10.1016/j.eswa.2018.08.020 - 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
- CoRec: a co-training approach for recommender systems
- Group-based collaborative filtering supported by multiple users' feedback to improve personalized ranking
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Informações sobre o DOI: 10.1016/j.eswa.2018.08.020 (Fonte: oaDOI API)
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