A Cooking Recipe Multi-Label Classification Approach for Food Restriction Identification
Recipe sharing websites have become very popular in the past years, allowing individuals to use such systems in an attempt to find a desired recipe. But sometimes finding recipes which best fit the user's wishes, while still satisfying his food restrictions, may become a very time consuming and difficult task. In this work, we propose a recipe multi-label classification approach as part of a recipe recommendation system for people with food restrictions, in an attempt to automatically identify whether an input recipe or list of ingredients fits into one or more food restrictions, satisfying both user's expectations and needs. The experimental evaluation includes two approaches for feature selection, as a manner to reduce the computational costs for the proposed system.
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), 1:436–447.
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., and Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108–122.
Criminisi, A., Shotton, J., and Konukoglu, E. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning [internet]. Microsoft Research.
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.
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
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. arXiv preprint arXiv:1909.00105.
Mitchell, T. M. et al. (1997). Machine learning. wcb.
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.
Nilesh, N., Kumari, M., Hazarika, P., and Raman, V. (2019). Recommendation of indian cuisine recipes based on ingredients. In 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), pages 96–99. 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), 1:238–249.
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.
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 ddientes. In Symposium in Information and Human Language Technology (STIL 2019), volume 1, pages 81–85. SBC.
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.
Rong, C., Liu, Z., Huo, N., and Sun, H. (2019). Exploring chinese dietary habits using recipes extracted from websites. IEEE Access, 7:24354–24361.
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.
Zhang, L., Zhao, J., Li, S., Shi, B., and Duan, L.-Y. (2019). From market to dish: Multiingredient image recognition for personalized recipe recommendation. In 2019 IEEE International Conference on Multimedia and Expo (ICME), pages 1252–1257. IEEE.