Automatic Cooking Recipe Generation for People with Food Restrictions

• Larissa F. S. Britto UFRPE
• Luciano D. S. Pacífico UFRPE
• Teresa B. Ludermir UFPE

Abstract

Even with the increasing number of web pages dedicated to sharing culinary recipes, users may still have difficulty to find specific dishes due to the massive amount of data available in such repositories. These websites have resorted to recommendation systems to make the process of finding the ideal recipe easy to the users. However, the problem persists for people who have dietary restrictions or food intolerances, due to the reduced number of recipes aimed to such public. In this work we propose an automatic recipe generation approach, based on the substitution of ingredients using linear classifiers, in an attempt to help these users finding recipes that contemplate both their desires and food restrictions.

Keywords: Food Recipe Generation, Food Restrictions, Machine Learning, Text Analysis

References

Blitzer, J., Dredze, M., and Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification.

Blitzer, J., McDonald, R., and Pereira, F. (2006). Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 120–128, Sydney, Australia. Association for Computational Linguistics.

Britto, L., Oliveira, E., Pacıfico, L., and Ludermir, T. (2019). Uma abordagem de analise de textos para a classificacao de receitas culinarias baseadas em documentos em portugues brasileiro. In Anais do XVI Encontro Nacional de Inteligˆencia Artificial e Computacional, pages 436–447, Porto Alegre, RS, Brasil. SBC.

Britto, L. and Pacıfico, L. (2019). Analise de sentimentos para revisoes de aplicativos mobile em portugues brasileiro. In Anais do XVI Encontro Nacional de Inteligencia Artificial e Computacional, pages 1080–1090, Porto Alegre, RS, Brasil. SBC.

Britto, L. F. S., Lima, R., and Pacıfico, L. D. S. (2019). Structural correspondence learning for cross-domain sentiment analysis in brazilian portuguese. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pages 812–817.

Britto, L. F. S., Pacıfico, L. D. S., and Ludermir, T. B. (2019). Sistemas de recomendacao de receitas e dietas atraves de metodos de aprendizagem de maquina. In Anais do I Encontro de Biociˆencias da UFPE (I EBIO UFPE).

Gorbonos, E., Liu, Y., and Hoang, C. T. (2018). Nutrec: Nutrition oriented online recipe recommender. In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 25–32. IEEE.

Isinkaye, F., Folajimi, Y., and Ojokoh, B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3):261–273.

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.

Majumder, B. P., Li, S., Ni, J., and McAuley, J. (2019). Generating personalized recipes from historical user preferences. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5976–5982, Hong Kong, China. Association for Computational Linguistics.

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.

Oliveira, E., Britto, L., Pacıfico, L., and Ludermir, T. (2019). Recomendacao e geracao de receitas baseada na substituicao de ingredientes. In Anais do XVI Encontro Nacional de Inteligencia Artificial e Computacional, pages 238–249, Porto Alegre, RS, Brasil. SBC.

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.

Pacıfico, L. S. D., Oliveira, E. G., Britto, L. F. S., and Ludermir, T. B. (2019). Sistemas de recomendacao e geracao de receitas atraves da categorizacao ontologica dos ingredientes. In Proceedings of the XII Brazilian Symposium in Information and Human Language Technology, pages 80–85.

Shino, N., Yamanishi, R., and Fukumoto, J. (2016). Recommendation system for alternative-ingredients based on co-occurrence relation on recipe database and the ingredient category. In 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pages 173–178. IEEE.

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.

Ziser, Y. and Reichart, R. (2016). Neural structural correspondence learning for domain adaptation. CoRR, abs/1610.01588.