Cooking Recipe Generation for Users with Food Restrictions by Automatic Ingredient Substitution
Abstract
In this work, a data-driven approach for restriction-free cooking recipe generation is presented. The proposed system is composed by a filtering process, where a recipe containing a forbidden ingredient, for a group of users, is automatically adapted to a food restriction domain by single ingredient substitution, helping to improve the number of recipes available for those users. The proposed filtering process is evaluated by means of a qualitative analysis, showing promising results.
Keywords:
Cooking Recipe Generation, Recipe Recommendation, Ingredient Substitution, Text Analysis
References
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Majumder, B. P., Li, S., Ni, J., and McAuley, J. (2019). Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105
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
Pecune, F., Callebert, L., and Marsella, S. (2020). A socially-aware conversational recommender system for personalized recipe recommendations. In Proceedings of the 8th International Conference on Human-Agent Interaction, pages 78–86
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
Twomey, N., Fain, M., Ponikar, A., and Sarraf, N. (2020). Towards multi-language recipe personalisation and recommendation. In Fourteenth ACM Conference on Recommender Systems, pages 708-713
Jiang, H., Wang, W., Liu, M., Nie, L., Duan, L.-Y., and Xu, C. (2019). Market2dish: A health-aware food recommendation system. In Proceedings of the 27th ACM International Conference on Multimedia, pages 2188–2190
Majumder, B. P., Li, S., Ni, J., and McAuley, J. (2019). Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105
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
Pecune, F., Callebert, L., and Marsella, S. (2020). A socially-aware conversational recommender system for personalized recipe recommendations. In Proceedings of the 8th International Conference on Human-Agent Interaction, pages 78–86
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
Twomey, N., Fain, M., Ponikar, A., and Sarraf, N. (2020). Towards multi-language recipe personalisation and recommendation. In Fourteenth ACM Conference on Recommender Systems, pages 708-713
Published
2021-07-18
How to Cite
PACÍFICO, Luciano D. S.; BRITTO, Larissa F. S.; LUDERMIR, Teresa B..
Cooking Recipe Generation for Users with Food Restrictions by Automatic Ingredient Substitution. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 183-190.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15821.
