This paper presents AdaptaFood, a system to adapt recipes to specific dietary constraints. 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.