Service-oriented multi-platform for food computing: A mobile application for recipe adaptation to nutrition behaviours (AI2Cuisine)

Supporting users in their food choices for mindful eating has become one of the spotlights for investigating modern food systems. However, building integral food platforms for this purpose is challenging due to dealing with heterogeneous data sources of different scopes, such as recipe data, food data, and user and dietary specificities. This research paper presents a versatile multi-platform architecture based on micro-services for dealing with different food-related tasks. The contributions of this research are manifold: (1) Firstly, we propose an architecture that enables us to handle various food-related tasks while managing various food data sources, providing data standardisation, scalability, and security benefits; (2) We introduce a novel recipe adaptation algorithm based on intelligent search in external resources and intelligent adaptation of the recipe preparation; (3) We include AI2Cuisine, a mobile application for recipe adaptation to meet different requirements like preferences, health and sustainable goals; (4) Finally, we perform an analysis and discussion in term of the necessity and impact of such sort of applications on the population. To demonstrate the feasibility of our proposal, we have conducted an experimental evaluation, and the results have been validated for various end-users with different expertise. • We present a multi-platform and microservices-based architecture for healthy nutrition food systems. • We introduce a novel and intelligent recipe adaptation algorithm. • We re-train the RoBERTa model to the food-domain. • We present a mobile application (AI2Cuisine) for adapting recipes considering dietary preferences and allergies. • Our study showcases the necessity and potential impact of such applications.