Automatic Recipe Ingredient Substitution Based on Text Mining and Data Clustering Approaches
Resumo
With the advance of Information Technologies, recipe sharing websites have become very common, and people can use such systems in an attempt to find a recipe that fits both their desires and nutritional needs. But sometimes ingredients from a given recipe list may not be available, what may require some adaptations by replacing the missing ingredients. In this work, a data-driven approach is employed to develop a new recipe generation system, that recommends substitute ingredients to adapt recipes into a target domain. The proposed system, which is based on Text Mining and Data Clustering techniques, is evaluated by means of a qualitative analysis, showing promising results.
Palavras-chave:
Data Clustering, Natural Language Processing, Recommendation Systems, Text Mining
Referências
Ławrynowicz, A., Wróblewska, A., Adrian, W. T., Kulczyński, B., and Gramza-Michałowska, A. Food recipe ingredient substitution ontology design pattern. Sensors 22 (3): 1095, 2022.
MacQueen, J. et al. Some methods for classication and analysis of multivariate observations. In Proceedings of the fth Berkeley symposium on mathematical statistics and probability. Vol. 1. California, USA, pp. 281-297, 1967.
Majumder, B. P., Li, S., Ni, J., and McAuley, J. Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105, 2019.
Nirmal, I., Caldera, A., and Bandara, R. D. Optimization framework for avour and nutrition balanced recipe: A data driven approach. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, pp. 19, 2018.
Ooi, A., Iiba, T., and Takano, K. Ingredient substitute recommendation for allergy-safe cooking based on food context. In 2015 IEEE Pacic Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, pp. 444-449, 2015.
Pacifico, L. D. S., Britto, L. F. S., and Ludermir, T. B. Ingredient substitute recommendation based on collaborative ltering and recipe context for automatic allergy-safe recipe generation. In Proceedings of the Brazilian Symposium on Multimedia and the Web. WebMedia '21. Association for Computing Machinery, pp. 97-104, 2021.
Pecune, F., Callebert, L., and Marsella, S. A socially-aware conversational recommender system for personalized recipe recommendations. In Proceedings of the 8th International Conference on Human-Agent Interaction. HAI '20. Association for Computing Machinery, pp. 78-86, 2020.
Shino, N., Yamanishi, R., and Fukumoto, J. 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). IEEE, pp. 173-178, 2016.
MacQueen, J. et al. Some methods for classication and analysis of multivariate observations. In Proceedings of the fth Berkeley symposium on mathematical statistics and probability. Vol. 1. California, USA, pp. 281-297, 1967.
Majumder, B. P., Li, S., Ni, J., and McAuley, J. Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105, 2019.
Nirmal, I., Caldera, A., and Bandara, R. D. Optimization framework for avour and nutrition balanced recipe: A data driven approach. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, pp. 19, 2018.
Ooi, A., Iiba, T., and Takano, K. Ingredient substitute recommendation for allergy-safe cooking based on food context. In 2015 IEEE Pacic Rim Conference on Communications, Computers and Signal Processing (PACRIM). IEEE, pp. 444-449, 2015.
Pacifico, L. D. S., Britto, L. F. S., and Ludermir, T. B. Ingredient substitute recommendation based on collaborative ltering and recipe context for automatic allergy-safe recipe generation. In Proceedings of the Brazilian Symposium on Multimedia and the Web. WebMedia '21. Association for Computing Machinery, pp. 97-104, 2021.
Pecune, F., Callebert, L., and Marsella, S. A socially-aware conversational recommender system for personalized recipe recommendations. In Proceedings of the 8th International Conference on Human-Agent Interaction. HAI '20. Association for Computing Machinery, pp. 78-86, 2020.
Shino, N., Yamanishi, R., and Fukumoto, J. 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). IEEE, pp. 173-178, 2016.
Publicado
28/11/2022
Como Citar
PACÍFICO, Luciano D. S.; BRITTO, Larissa F. S.; LUDERMIR, Teresa B..
Automatic Recipe Ingredient Substitution Based on Text Mining and Data Clustering Approaches. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 82-89.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2022.226893.
