A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes
MetadataShow full item record
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Data mappingFood computingNatural language processingRecipe adaptationWord embedding
A. Morales-Garzón, J. Gómez-Romero and M. J. Martin-Bautista, "A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes," in IEEE Access, vol. 9, pp. 27389-27404, 2021, doi: 10.1109/ACCESS.2021.3058559.
SponsorshipEuropean Commission 816303; University of Granada
Studying food recipes is indispensable to understand the science of cooking. An essential problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty when adapting recipes is in determining ingredients relations, which are compound and hard to interpret. Word embedding models can catch the semantics of food items in a recipe, helping to understand how ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting ingredient recipes to user preferences. To learn food representations and relations, we create and apply a specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a specific-domain semantic model to tackle the recipe adaptation task.