Recipe Recommendation and Generation Based on Ingredient Substitution

• Emilia Oliveira Universidade Federal Rural de Pernambuco
• Larissa Britto Universidade Federal Rural de Pernambuco
• Luciano Pacífico Universidade Federal Rural de Pernambuco
• Teresa Ludermir Universidade Federal de Pernambuco

DOI: https://doi.org/10.5753/eniac.2019.9287

Abstract

Nowadays, even with the increasing number of recipe sharing websites and systems, users may have difficult to search specific dishes through the massive amount of data contained in such repositories. Also, finding recipes which best fit a handy set of ingredients, while at the same time contemplate some user wishes and restrictions, may become a very time consuming or even impossible task. In this work, we propose a new recipe recommendation and generation system, based on the substitution of recipe ingredients and a data-driven approach, in an attempt to help users finding a recipe that contemplates both their desires and food restrictions, avoiding food wastes.

Keywords: Recipe Recommendation, Automatic Recipe Generation, Ingredient Substitution, Text Analysis

References

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.

Freyne, J. and Berkovsky, S. (2010). Intelligent food planning: personalized recipe recommendation. In Proceedings of the 15th international conference on Intelligent user interfaces, pages 321–324. ACM.

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.

Hammond, K. J. (1986). Chef: A model of case-based planning. In AAAI, pages 267–271.

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.

Manning, C., Raghavan, P., and Schütze, H. (2010). Introduction to information retrieval. Natural Language Engineering, 16(1):100–103.

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.

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.

Svensson, M., Höök, K., and Cöster, R. (2005). Designing and evaluating kalas: A social navigation system for food recipes. ACM Transactions on Computer-Human Interaction (TOCHI), 12(3):374–400.

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.