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Filtros : "Souza, Rodrigo Ferrari de" Limpar

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  • Artigo de periodico

    Digitally nudging users to explore off-profile recommendations: here be dragons (2024)

    Alves, Gabrielle Aparecida Pires; Jannach, Dietmar ; Souza, Rodrigo Ferrari de ; Damian, Daniela ; Manzato, Marcelo Garcia

    Source: User Modeling and User-Adapted Interaction. Unidade: ICMC

    Assunto: SISTEMAS DE RECOMENDAÇÃO

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      ALVES, Gabrielle Aparecida Pires et al. Digitally nudging users to explore off-profile recommendations: here be dragons. User Modeling and User-Adapted Interaction, v. 34, n. 2, p. 441-481, 2024Tradução . . Disponível em: https://doi.org/10.1007/s11257-023-09378-7. Acesso em: 30 abr. 2024.

    • APA

      Alves, G. A. P., Jannach, D., Souza, R. F. de, Damian, D., & Manzato, M. G. (2024). Digitally nudging users to explore off-profile recommendations: here be dragons. User Modeling and User-Adapted Interaction, 34( 2), 441-481. doi:10.1007/s11257-023-09378-7

    • NLM

      Alves GAP, Jannach D, Souza RF de, Damian D, Manzato MG. Digitally nudging users to explore off-profile recommendations: here be dragons [Internet]. User Modeling and User-Adapted Interaction. 2024 ; 34( 2): 441-481.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1007/s11257-023-09378-7

    • Vancouver

      Alves GAP, Jannach D, Souza RF de, Damian D, Manzato MG. Digitally nudging users to explore off-profile recommendations: here be dragons [Internet]. User Modeling and User-Adapted Interaction. 2024 ; 34( 2): 441-481.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1007/s11257-023-09378-7

  • Trabalho de evento

    Counteracting popularity-bias and improving diversity through calibrated recommendations (2023)

    Sacilotti, Andre ; Souza, Rodrigo Ferrari de ; Manzato, Marcelo Garcia

    Source: Proceedings. Conference titles: International Conference on Enterprise Information Systems - ICEIS. Unidade: ICMC

    Subjects: SISTEMAS DE RECOMENDAÇÃO, APRENDIZADO COMPUTACIONAL

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • 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: 30 abr. 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:10.5220/0011846000003467

    • NLM

      Sacilotti A, Souza RF de, Manzato MG. Counteracting popularity-bias and improving diversity through calibrated recommendations [Internet]. Proceedings. 2023 ;[citado 2024 abr. 30 ] Available from: https://doi.org/10.5220/0011846000003467

    • Vancouver

      Sacilotti A, Souza RF de, Manzato MG. Counteracting popularity-bias and improving diversity through calibrated recommendations [Internet]. Proceedings. 2023 ;[citado 2024 abr. 30 ] Available from: https://doi.org/10.5220/0011846000003467
  • 1

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