Uma Abordagem de Análise de Textos para a Classificação de Receitas Culinárias Baseadas em Documentos em Português Brasileiro
Resumo
Neste trabalho, a classificação de receitas culinárias é abordada através da elaboração de uma ferramenta computacional própria para a análise de documentos textuais escritos em Português. A ferramenta proposta será parte fundamental no desenvolvimento de sistemas de recomendação de receitas para os brasileiros, no intuito do incentivo à prática de hábitos alimentares saudáveis por essa população. Uma base de dados nova, obtida através de páginas web brasileiras, é elaborada e testada pelo uso de algoritmos obtidos da literatura de Aprendizagem de Máquina. Experimentos foram efetuados no intuito da seleção dos melhores classificadores para a composição dos módulos de reconhecimento dos sistemas de recomendação a serem desenvolvidos.
Palavras-chave:
Classificação Automática de Receitas, Sistemas de Recomendação de Receitas, Aprendizagem Supervisionada, Análise de Textos
Referências
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de Souza, J. G. R., de Paiva Oliveira, A., and Moreira, A. (2018). Development of a brazilian portuguese hotel’s reviews corpus. In International Conference on Computational Processing of the Portuguese Language, pages 353–361. Springer.
De Stefano, C., Fontanella, F., and Di Freca, A. S. (2012). A novel naive bayes voting strategy for combining classifiers. In Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on, pages 467–472. IEEE.
Gorbonos, E., Liu, Y., and Hoàng, C. T. (2018). Nutrec: Nutrition oriented online recipe recommender. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 25–32. IEEE.
Hamoud, A. A., Alwehaibi, A., Roy, K., and Bikdash, M. (2018). Classifying political tweets using naı̈ve bayes and support vector machines. In Recent Trends and Future Tchnology in Applied Intelligence, pages ”736–744”. Springer International Publishing.
Haykin, S. S. (2001). Neural networks: a comprehensive foundation. Tsinghua University Press.
Jayaraman, S., Choudhury, T., and Kumar, P. (2017). Analysis of classification models based on cuisine prediction using machine learning. In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), pages 1485–1490. IEEE.
Kalajdziski, S., Radevski, G., Ivanoska, I., Trivodaliev, K., and Stojkoska, B. R. (2018). Cuisine classification using recipe’s ingredients. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1074–1079. IEEE.
Mokdara, T., Pusawiro, P., and Harnsomburana, J. (2018). Personalized food recommendation using deep neural network. In 2018 Seventh ICT International Student Project Conference (ICT-ISPC), pages 1–4. IEEE.
Nezis, A., Papageorgiou, H., Georgiadis, P., Jiskra, P., Pappas, D., and Pontiki, M. (2018). Towards a fully personalized food recommendation tool. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces, page 77. ACM.
Nirmal, I., Caldera, A., and Bandara, R. D. (2018). Optimization framework for flavour and nutrition balanced recipe: A data driven approach. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pages 1–9. IEEE.
Ooi, A., Iiba, T., and Takano, K. (2015). Ingredient substitute recommendation for allergy-safe cooking based on food context. In 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), pages 444–449. IEEE.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning internal representations by error propagation. Technical report, California Univ San Diego La Jolla Inst for Cognitive Science.
Su, H., Lin, T.-W., Li, C.-T., Shan, M.-K., and Chang, J. (2014). Automatic recipe cuisine classification by ingredients. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: adjunct publication, pages 565–570. ACM.
Trattner, C. and Elsweiler, D. (2017). Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In Proceedings of the 26th international conference on world wide web, pages 489–498. International World Wide Web Conferences Steering Committee.
Yasukawa, M. and Scholer, F. (2017). Concurrence of word concepts in cooking recipe search. In Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence, pages 25–30. ACM.
Criminisi, A., Shotton, J., and Konukoglu, E. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning [internet]. Microsoft Research.
de Souza, J. G. R., de Paiva Oliveira, A., and Moreira, A. (2018). Development of a brazilian portuguese hotel’s reviews corpus. In International Conference on Computational Processing of the Portuguese Language, pages 353–361. Springer.
De Stefano, C., Fontanella, F., and Di Freca, A. S. (2012). A novel naive bayes voting strategy for combining classifiers. In Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on, pages 467–472. IEEE.
Gorbonos, E., Liu, Y., and Hoàng, C. T. (2018). Nutrec: Nutrition oriented online recipe recommender. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 25–32. IEEE.
Hamoud, A. A., Alwehaibi, A., Roy, K., and Bikdash, M. (2018). Classifying political tweets using naı̈ve bayes and support vector machines. In Recent Trends and Future Tchnology in Applied Intelligence, pages ”736–744”. Springer International Publishing.
Haykin, S. S. (2001). Neural networks: a comprehensive foundation. Tsinghua University Press.
Jayaraman, S., Choudhury, T., and Kumar, P. (2017). Analysis of classification models based on cuisine prediction using machine learning. In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), pages 1485–1490. IEEE.
Kalajdziski, S., Radevski, G., Ivanoska, I., Trivodaliev, K., and Stojkoska, B. R. (2018). Cuisine classification using recipe’s ingredients. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1074–1079. IEEE.
Mokdara, T., Pusawiro, P., and Harnsomburana, J. (2018). Personalized food recommendation using deep neural network. In 2018 Seventh ICT International Student Project Conference (ICT-ISPC), pages 1–4. IEEE.
Nezis, A., Papageorgiou, H., Georgiadis, P., Jiskra, P., Pappas, D., and Pontiki, M. (2018). Towards a fully personalized food recommendation tool. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces, page 77. ACM.
Nirmal, I., Caldera, A., and Bandara, R. D. (2018). Optimization framework for flavour and nutrition balanced recipe: A data driven approach. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pages 1–9. IEEE.
Ooi, A., Iiba, T., and Takano, K. (2015). Ingredient substitute recommendation for allergy-safe cooking based on food context. In 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), pages 444–449. IEEE.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning internal representations by error propagation. Technical report, California Univ San Diego La Jolla Inst for Cognitive Science.
Su, H., Lin, T.-W., Li, C.-T., Shan, M.-K., and Chang, J. (2014). Automatic recipe cuisine classification by ingredients. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: adjunct publication, pages 565–570. ACM.
Trattner, C. and Elsweiler, D. (2017). Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In Proceedings of the 26th international conference on world wide web, pages 489–498. International World Wide Web Conferences Steering Committee.
Yasukawa, M. and Scholer, F. (2017). Concurrence of word concepts in cooking recipe search. In Proceedings of the 9th Workshop on Multimedia for Cooking and Eating Activities in conjunction with The 2017 International Joint Conference on Artificial Intelligence, pages 25–30. ACM.
Publicado
15/10/2019
Como Citar
BRITTO, Larissa; OLIVEIRA, Emilia; PACÍFICO, Luciano; LUDERMIR, Teresa.
Uma Abordagem de Análise de Textos para a Classificação de Receitas Culinárias Baseadas em Documentos em Português Brasileiro. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 436-447.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9304.
