An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning (2024)
- Authors:
- Autor USP: CURASMA, HERMINIO PAUCAR - ICMC
- Unidade: ICMC
- DOI: 10.34117/bjdv10n1-050
- Subjects: SISTEMAS DE RECOMENDAÇÃO; APRENDIZAGEM PROFUNDA; COMPUTAÇÃO EM NUVEM
- Keywords: programming online judges; collaborative filtering; programando juízes on-line; filtragem colaborativa
- Language: Inglês
- Imprenta:
- Source:
- Título: Brazilian Journal of Development
- ISSN: 2525-8761
- Volume/Número/Paginação/Ano: v. 10, n. 1, p. 790-806, jan. 2024
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
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ABNT
JULCA-MEJIA, Wilson e PAUCAR-CURASMA, Herminio. An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning. Brazilian Journal of Development, v. 10, n. ja 2024, p. 790-806, 2024Tradução . . Disponível em: https://doi.org/10.34117/bjdv10n1-050. Acesso em: 10 jan. 2026. -
APA
Julca-Mejia, W., & Paucar-Curasma, H. (2024). An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning. Brazilian Journal of Development, 10( ja 2024), 790-806. doi:10.34117/bjdv10n1-050 -
NLM
Julca-Mejia W, Paucar-Curasma H. An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning [Internet]. Brazilian Journal of Development. 2024 ; 10( ja 2024): 790-806.[citado 2026 jan. 10 ] Available from: https://doi.org/10.34117/bjdv10n1-050 -
Vancouver
Julca-Mejia W, Paucar-Curasma H. An extended study of a cloud-based recommender system for competitive programming platforms with machine and deep learning [Internet]. Brazilian Journal of Development. 2024 ; 10( ja 2024): 790-806.[citado 2026 jan. 10 ] Available from: https://doi.org/10.34117/bjdv10n1-050 - Reactive software architectures in IoT: a literature review
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Informações sobre o DOI: 10.34117/bjdv10n1-050 (Fonte: oaDOI API)
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