Nutritional Value Prediction System for Food Recipes Using Machine Learning

• Luciano D. S. Pacifico UFRPE http://orcid.org/0000-0001-6945-3612
• Emília G. de Oliveira UFRPE
• Larissa F. S. Britto UFRPE
• Teresa B. Ludermir UFPE http://orcid.org/0000-0002-8980-6742

DOI: https://doi.org/10.5753/bresci.2020.11191

Abstract

In this work, a system to predict nutritional values for food recipes is proposed. An automatic and data-driven approach to estimate nutritional values for new recipes is employed, and Machine Learning techniques. The proposed prediction system will be a central piece on the design of new Recipe Recommendation Systems, where complete personal dietary plans, contemplating both the user nutritional needs (according to daily nutrition standards) and expectations, are going to be fulfilled.

Keywords: Food Recipe Nutritional Values, Recipe Recommendation Systems, Machine Learning, Text Analysis

References

Britto, L. F. S., Oliveira, E. G., Pacifico, L. D. S., and Ludermir, T. B. (2019). A text analysis approach for cooking recipe classification based on brazilian portuguese documents. Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2019),1:1–12.

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.

Haykin, S. (1994). Neural networks: a comprehensive foundation. Prentice Hall PTR.

Majumder, B. P., Li, S., Ni, J., and McAuley, J. (2019). Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105.

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.

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. G., Britto, L. F. S., Pacifico, L. D. S., and Ludermir, T. B. (2019). Recipe recommendation and generation based on ingredient substitution. Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2019), 1:1–12.

Pacifico, L. D. S., Oliveira, E. G., Britto, L. F. S., and Ludermir, T. B. (2019). Sistemas de recomendação e geração de receitas através da categorização ontológica dos ingredientes. In Symposium in Information and Human Language Technology (STIL 2019), volume 1, pages 81–85. SBC.

Toledo, R. Y., Alzahrani, A. A., and Martínez, L. (2019). A food recommender system considering nutritional information and user preferences. IEEE Access, 7:96695–96711.

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.