Towards Automated Recipe Reconstruction

Abstract

Accurate and scalable collection of dietary data is vital for advancing nutritional epidemiology and understanding links between diet, public health, and environmental sustainability. A key challenge is the collection of the detailed nutrition data on the product level which currently largely relies on manual recipe reconstruction. <br/> We propose computational approaches to optimize this workflow. First, an information retrieval (IR)–based recommender system integrates food-category prediction with retrieval over product text, ingredients, and nutrient profiles to streamline food item matching and reduce redundancy across the database. Second, we outline a roadmap for automated recipe reconstruction that combines large language models (LLMs) for ingredient parsing with nutrient-constrained mathematical optimization for recipes reconstruction. <br/> By integrating machine learning, generative modeling, and optimization, our work enhances the efficiency, transparency, and scalability of nutrition data collection, laying a foundation for sustainable practices in nutritional epidemiology and research on interactions of the diet, health and environment.