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This is a common societal issue due to various dietary needs arising from medical conditions, allergies, or nutritional preferences. AdaptaFood provides recipe adaptations from two inputs: a recipe image (a fine-tuned image-captioning model allows us to extract the ingredients) or a recipe object (we extract the ingredients from the recipe features). For the adaptation, we propose to use an attention-based language sentence model based on BERT to learn the semantics of the ingredients and, therefore, discover the hidden relations among them. Specifically, we use them to perform two tasks: (1) align the food items from several sources to expand recipe information; (2) use the semantic features embedded in the representation vector to detect potential food substitutes for the ingredients. The results show that the model successfully learns domain-specific knowledge after re-training it to the food computing domain. Combining this acquired knowledge with the adopted strategy for sentence representation and food replacement enables the generation of high-quality recipe versions and dealing with the heterogeneity of different-origin food data.<\/jats:p>","DOI":"10.1007\/s00530-025-01667-y","type":"journal-article","created":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T17:47:16Z","timestamp":1738432036000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptafood: an intelligent system to adapt recipes to specialised diets and healthy lifestyles"],"prefix":"10.1007","volume":"31","author":[{"given":"Andrea","family":"Morales-Garz\u00f3n","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karel","family":"Guti\u00e9rrez-Batista","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria J.","family":"Martin-Bautista","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"issue":"5","key":"1667_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3329168","volume":"52","author":"W Min","year":"2019","unstructured":"Min, W., Jiang, S., Liu, L., Rui, Y., Jain, R.: A survey on food computing. 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