Counteracting popularity-bias and improving diversity through calibrated recommendations (2023)
Fonte: Proceedings. Nome do evento: International Conference on Enterprise Information Systems - ICEIS. Unidade: ICMC
Assuntos: SISTEMAS DE RECOMENDAÇÃO, APRENDIZADO COMPUTACIONAL
ABNT
SACILOTTI, Andre e SOUZA, Rodrigo Ferrari de e MANZATO, Marcelo Garcia. Counteracting popularity-bias and improving diversity through calibrated recommendations. 2023, Anais.. Setúbal: SciTePress, 2023. Disponível em: https://doi.org/10.5220/0011846000003467. Acesso em: 09 out. 2024.APA
Sacilotti, A., Souza, R. F. de, & Manzato, M. G. (2023). Counteracting popularity-bias and improving diversity through calibrated recommendations. In Proceedings. Setúbal: SciTePress. doi:10.5220/0011846000003467NLM
Sacilotti A, Souza RF de, Manzato MG. Counteracting popularity-bias and improving diversity through calibrated recommendations [Internet]. Proceedings. 2023 ;[citado 2024 out. 09 ] Available from: https://doi.org/10.5220/0011846000003467Vancouver
Sacilotti A, Souza RF de, Manzato MG. Counteracting popularity-bias and improving diversity through calibrated recommendations [Internet]. Proceedings. 2023 ;[citado 2024 out. 09 ] Available from: https://doi.org/10.5220/0011846000003467