Abstract
Food and culinary activities related to cooking are present in our daily lives. The rise of food-related data has led to the term food computing, which refers to the study and development of computer systems to solve food-related tasks. Despite the large number of food computing systems focused on the collection, recommendation, retrieval, and creation of recipes, very few have used existing recipes to get adapted versions for user requirements. In this work, we have developed a method for adapting recipes that suggests food options for substituting their ingredients based on food relations and text similarity. For this purpose, we employ different deep learning language models based on BERT. These models incorporate attention mechanisms to extract contextual representations of foods using different strategies to build the word embeddings. We use them to conduct a semantic comparison task for detecting similar ingredients between the recipe ingredients and a food dataset. The results show that the method obtains high-quality recipe versions, thanks to context data, attention mechanisms, and the token representation strategy used for the foods.
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Ahn, Y.Y., Ahnert, S.E., Bagrow, J.P., Barabási, A.L.: Flavor network and the principles of food pairing. Sci. Rep. 1(1), 1–7 (2011)
Altossar, J.: food2vec-augmented-cooking-machine intelligence. Jaan Altossar’s blog (2015). Accessed 17 December 2015
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: Semeval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055 (2017)
Chen, M., Jia, X., Gorbonos, E., Hong, C.T., Yu, X., Liu, Y.: Eating healthier: exploring nutrition information for healthier recipe recommendation. Inf. Process. Manag. 102051 (2019)
Fujita, J., Sato, M., Nobuhara, H.: Model for cooking recipe generation using reinforcement learning. In: 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW), pp. 1–4. IEEE (2021)
Harper, C., Siller, M.: OpenAG: a globally distributed network of food computing. IEEE Pervasive Comput. 14(4), 24–27 (2015). https://doi.org/10.1109/MPRV.2015.72
Harvey, M., Ludwig, B., Elsweiler, D.: You are what you eat: learning user tastes for rating prediction. In: Kurland, O., Lewenstein, M., Porat, E. (eds.) SPIRE 2013. LNCS, vol. 8214, pp. 153–164. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02432-5_19
Jiang, S., Min, W.: Food computing for multimedia. In: Proceedings of the 28th ACM International Conference on Multimedia. MM 2020, pp. 4782–4784. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3394171.3418544
Kazama, M., Sugimoto, M., Hosokawa, C., Matsushima, K., Varshney, L.R., Ishikawa, Y.: A neural network system for transformation of regional cuisine style. Front. ICT 5, 14 (2018)
Kim, K.J., Chung, C.H.: Tell me what you eat, and i will tell you where you come from: a data science approach for global recipe data on the web. IEEE Access 4, 8199–8211 (2016)
Majumder, B.P., Li, S., Ni, J., McAuley, J.: Generating personalized recipes from historical user preferences. arXiv preprint arXiv:1909.00105 (2019)
Marin, J., et al.: Recipe1m+: a dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 187–203 (2019)
McCance, R.A., Widdowson, E.M.: McCance and Widdowson’s the composition of foods. Roy/ Soc. Chem. (2014)
Metwally, A.A., Leong, A.K., Desai, A., Nagarjuna, A., Perelman, D., Snyder, M.: Learning personal food preferences via food logs embedding. arXiv preprint arXiv:2110.15498 (2021)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Min, W., Jiang, S., Jain, R.C.: Food recommendation: framework, existing solutions, and challenges. IEEE Trans. Multimedia 22, 2659–2671 (2020)
Min, W., Jiang, S., Liu, L., Rui, Y., Jain, R.: A survey on food computing. ACM Comput. Surv. (CSUR) 52(5), 1–36 (2019)
Min, W., Jiang, S., Sang, J., Wang, H., Liu, X., Herranz, L.: being a supercook: joint food attributes and multimodal content modeling for recipe retrieval and exploration. IEEE Trans. Multimedia 19(5), 1100–1113 (2016)
Min, W., Jiang, S., Wang, S., Sang, J., Mei, S.: A delicious recipe analysis framework for exploring multi-modal recipes with various attributes. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 402–410 (2017)
Morales-Garzón, A., Gómez-Romero, J., Martin-Bautista, M.J.: A word embedding-based method for unsupervised adaptation of cooking recipes. IEEE Access 9, 27389–27404 (2021)
Morales-Garzón, A., Gómez-Romero, J., Martin-Bautista, M.J.: A word embedding model for mapping food composition databases using fuzzy logic. In: Lesot, M.-J., et al. (eds.) IPMU 2020. CCIS, vol. 1238, pp. 635–647. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50143-3_50
World Health Organization et al.: Healthy diet. Technical report, World Health Organization. Regional Office for the Eastern Mediterranean (2019)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Reimers, N., Gurevych, I.: Sentence-Bert: sentence embeddings using Siamese Bert-networks. arXiv preprint arXiv:1908.10084 (2019)
Su, H., Lin, T.W., Li, C.T., Shan, M.K., Chang, J.: Automatic recipe cuisine classification by ingredients. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 565–570 (2014)
Toneva, M., Wehbe, L.: Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain). arXiv preprint arXiv:1905.11833 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Acknowledgements
This project is partially supported by the Andalusian government and the FEDER operative program under the project BigDataMed (P18-RT-2947 and B-TIC-145-UGR18). It is also supported by the Department of Economic Transformation, Industry, Knowledge and Universities of the Junta de Andalucía and the program of research initiation for master students of the University of Granada.
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Morales-Garzón, A., Gómez-Romero, J., Martín-Bautista, M.J. (2022). Contextual Sentence Embeddings for Obtaining Food Recipe Versions. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_24
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DOI: https://doi.org/10.1007/978-3-031-08974-9_24
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