Sistema de Recomendação do Portal gov.br
Resumo
Este artigo aborda a construção do sistema de recomendação do Portal gov.br, que melhora a experiência do cidadão ao oferecer serviços digitais de seu interesse. Decisões arquiteturais e detalhes de implementação da solução proposta, baseada em técnicas de recuperação de informação, são discutidas. Resultados experimentais indicam que a solução apresenta a melhor relação custo-benefício em comparação com uma técnica tradicional de recomendação baseada em fatoração de matrizes.
Palavras-chave:
sistemas de recomendação, portal gov.br, governo eletrônico
Referências
AGGARWAL, C. C. (2016) “Recommender systems”. In Springer International Publishing, Volume 1.
AL-HASSAN, M., LU, H., & LU, J. (2009) “A framework for delivering personalized e-government services from a citizen-centric approach”. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, p. 436-440.
AYACHI, R. et al. (2016) “Proactive and reactive e-government services recommendation”. In Universal Access in the Information Society, 15(4), p. 681-697.
BAEZA-YATES, R.; RIBEIRO NETO, B. (1999). “Modern information retrieval”, Volume 463.
FERREIRA, F. et al. (2020) “Investigating Multimodal Features for Video Recommendations at Globoplay”. In Fourteenth ACM Conference on Recommender Systems, p. 571-572.
GADEPALLY, V. N. et al. (2016) “Recommender systems for the department of defense and intelligence community”. In Lincoln Laboratory Journal, 22(1).
GOMEZ-URIBE, C. A.; HUNT, N. (2015) “The netflix recommender system: Algorithms, business value, and innovation”. In ACM Transactions on Management Information Systems (TMIS), 6(4), p. 1-19.
GOVERNO DO BRASIL - A. (2022) “O que é o gov.br”. Disponível em: <https://www.gov.br/sobre>. Acesso em 19 de março de 2022.
GOVERNO DO BRASIL - B. (2022) “Painel de monitoramento de serviços federais”. Disponível em: <https://painelservicos.servicos.gov.br/>. Acesso em 19 de março de 2022.
GUTPA, U. et al. (2020) “The architectural implications of facebook's dnn-based personalized recommendation”. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), p. 488-501.
HU, Y.; KOREN, Y.; VOLINSKY, C. (2008) “Collaborative filtering for implicit feedback datasets”. In Eighth IEEE international conference on data mining, p. 263-272.
INVESP (2022) “The Average Website Conversion Rate by Industry”. Disponível em: <https://www.invespcro.com/blog/the-average-website-conversion-rate-by-industry>. Acesso em 18 de maio de 2022.
KOREN, Y.; BELL, R. (2015) “Advances in collaborative filtering”. In Recommender systems handbook, p. 77-118.
LINDEN, G.; SMITH, B.; YORK, J. (2003) “Amazon.com recommendations: Item-to-item collaborative filtering”. In IEEE Internet computing, 7(1), p. 76-80.
LOGESH, R.; SUBRAMANIYASWAMY, V.; VIJAYAKUMAR, V. (2018) “A personalised travel recommender system utilising social network profile and accurate GPS data”. In Electronic Government, an International Journal, 14(1), p. 90-113.
LU, J. et al. (2010) “BizSeeker: a hybrid semantic recommendation system for personalized government-to-business e-services”. In Internet Research.
LU et al. (2013) “A web-based personalized business partner recommendation system using fuzzy semantic techniques”. In Computational Intelligence, 29(1), p. 37-69.
MACEDO, D. et al. (2020) “Uma ferramenta para recomendação de visualização de dados governamentais abertos”. In Anais do VIII Workshop de Computação Aplicada em Governo Eletrônico, p. 96-107.
MOISINHO, A. M. et al. (2021) “Modelo Conceitual para Sistemas de Recomendação voltados a Governo Eletrônico com o uso do Cadastro Base do Cidadão”. In Anais do IX Workshop de Computação Aplicada em Governo Eletrônico, p. 215-226.
RESNICK, P.; VARIAN, H. R. (1997) “Recommender systems”. In Communications of the ACM, 40(3), p. 56-58.
RICCI, F.; ROKACH, L.; SHAPIRA, B. (2011) “Introduction to recommender systems handbook”. In Recommender systems handbook, p. 1-35. Springer, Boston, MA.
SEMERCI, O. et al. (2019) “Homepage personalization at spotify”. In Proceedings of the 13th ACM Conference on Recommender Systems, p. 527-527.
SUN, N. et al. (2021) “Enhanced Collaborative Filtering for Personalized E-Government Recommendation”. In Applied Sciences, 11(24), p. 12119.
AL-HASSAN, M., LU, H., & LU, J. (2009) “A framework for delivering personalized e-government services from a citizen-centric approach”. In Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, p. 436-440.
AYACHI, R. et al. (2016) “Proactive and reactive e-government services recommendation”. In Universal Access in the Information Society, 15(4), p. 681-697.
BAEZA-YATES, R.; RIBEIRO NETO, B. (1999). “Modern information retrieval”, Volume 463.
FERREIRA, F. et al. (2020) “Investigating Multimodal Features for Video Recommendations at Globoplay”. In Fourteenth ACM Conference on Recommender Systems, p. 571-572.
GADEPALLY, V. N. et al. (2016) “Recommender systems for the department of defense and intelligence community”. In Lincoln Laboratory Journal, 22(1).
GOMEZ-URIBE, C. A.; HUNT, N. (2015) “The netflix recommender system: Algorithms, business value, and innovation”. In ACM Transactions on Management Information Systems (TMIS), 6(4), p. 1-19.
GOVERNO DO BRASIL - A. (2022) “O que é o gov.br”. Disponível em: <https://www.gov.br/sobre>. Acesso em 19 de março de 2022.
GOVERNO DO BRASIL - B. (2022) “Painel de monitoramento de serviços federais”. Disponível em: <https://painelservicos.servicos.gov.br/>. Acesso em 19 de março de 2022.
GUTPA, U. et al. (2020) “The architectural implications of facebook's dnn-based personalized recommendation”. In 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA), p. 488-501.
HU, Y.; KOREN, Y.; VOLINSKY, C. (2008) “Collaborative filtering for implicit feedback datasets”. In Eighth IEEE international conference on data mining, p. 263-272.
INVESP (2022) “The Average Website Conversion Rate by Industry”. Disponível em: <https://www.invespcro.com/blog/the-average-website-conversion-rate-by-industry>. Acesso em 18 de maio de 2022.
KOREN, Y.; BELL, R. (2015) “Advances in collaborative filtering”. In Recommender systems handbook, p. 77-118.
LINDEN, G.; SMITH, B.; YORK, J. (2003) “Amazon.com recommendations: Item-to-item collaborative filtering”. In IEEE Internet computing, 7(1), p. 76-80.
LOGESH, R.; SUBRAMANIYASWAMY, V.; VIJAYAKUMAR, V. (2018) “A personalised travel recommender system utilising social network profile and accurate GPS data”. In Electronic Government, an International Journal, 14(1), p. 90-113.
LU, J. et al. (2010) “BizSeeker: a hybrid semantic recommendation system for personalized government-to-business e-services”. In Internet Research.
LU et al. (2013) “A web-based personalized business partner recommendation system using fuzzy semantic techniques”. In Computational Intelligence, 29(1), p. 37-69.
MACEDO, D. et al. (2020) “Uma ferramenta para recomendação de visualização de dados governamentais abertos”. In Anais do VIII Workshop de Computação Aplicada em Governo Eletrônico, p. 96-107.
MOISINHO, A. M. et al. (2021) “Modelo Conceitual para Sistemas de Recomendação voltados a Governo Eletrônico com o uso do Cadastro Base do Cidadão”. In Anais do IX Workshop de Computação Aplicada em Governo Eletrônico, p. 215-226.
RESNICK, P.; VARIAN, H. R. (1997) “Recommender systems”. In Communications of the ACM, 40(3), p. 56-58.
RICCI, F.; ROKACH, L.; SHAPIRA, B. (2011) “Introduction to recommender systems handbook”. In Recommender systems handbook, p. 1-35. Springer, Boston, MA.
SEMERCI, O. et al. (2019) “Homepage personalization at spotify”. In Proceedings of the 13th ACM Conference on Recommender Systems, p. 527-527.
SUN, N. et al. (2021) “Enhanced Collaborative Filtering for Personalized E-Government Recommendation”. In Applied Sciences, 11(24), p. 12119.
Publicado
31/07/2022
Como Citar
ALENCAR, Rafael Odon de; PITA, Marcelo; AGRA, Ronaldo.
Sistema de Recomendação do Portal gov.br. In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 10. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 85-96.
ISSN 2763-8723.
DOI: https://doi.org/10.5753/wcge.2022.223140.
