Exploiting different users' interactions for profiles enrichment in recommender systems (2016)
- Authors:
- USP affiliated authors: MANZATO, MARCELO GARCIA - ICMC ; CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1145/2851613.2851923
- Subjects: INTELIGÊNCIA ARTIFICIAL; SISTEMAS DE INFORMAÇÃO; WORLD WIDE WEB
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Symposium on Applied Computing - SAC
- Este artigo NÃO possui versão em acesso aberto
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Status: Nenhuma versão em acesso aberto identificada -
ABNT
COSTA, Arthur F. da et al. Exploiting different users' interactions for profiles enrichment in recommender systems. 2016, Anais.. New York: ACM, 2016. Disponível em: https://doi.org/10.1145/2851613.2851923. Acesso em: 17 mar. 2026. -
APA
Costa, A. F. da, Martins, R. D., Manzato, M. G., & Campello, R. J. G. B. (2016). Exploiting different users' interactions for profiles enrichment in recommender systems. In Proceedings. New York: ACM. doi:10.1145/2851613.2851923 -
NLM
Costa AF da, Martins RD, Manzato MG, Campello RJGB. Exploiting different users' interactions for profiles enrichment in recommender systems [Internet]. Proceedings. 2016 ;[citado 2026 mar. 17 ] Available from: https://doi.org/10.1145/2851613.2851923 -
Vancouver
Costa AF da, Martins RD, Manzato MG, Campello RJGB. Exploiting different users' interactions for profiles enrichment in recommender systems [Internet]. Proceedings. 2016 ;[citado 2026 mar. 17 ] Available from: https://doi.org/10.1145/2851613.2851923 - CoRec: a co-training approach for recommender systems
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- Case recommender: a flexible and extensible Python framework for recommender systems
- 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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