Towards Automated Recipe ReconstructionOptimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization
Towards Automated Recipe Reconstruction
Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization
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12.2025Verlag / herausgebende Einrichtung
IEEE, Institute of Electrical and Electronics EngineersVerlagsort
New York, NYMetadaten
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Zitierbarer Link (Handle URI) 
https://hdl.handle.net/20.500.11811/14070
Inhalt
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.
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.
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.
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.
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.
Bemerkung
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Schlagwörter
Nutrition Data Collection, Nutritional Epidemiology, Recipe Reconstruction, Information Retrieval, Food Categorization, Machine Learning, Large Language Models, Mathematical Optimization
Klassifikation (DDC)
004 InformatikErstpublikation
https://doi.org/10.1109/BigData66926.2025.11401661Konferenz
2025 IEEE International Conference on Big Data (BigData)Schmidt, Svetlana; Klasen, Linda; Nöthlings, Ute; Sifa, Rafet: Towards Automated Recipe Reconstruction : Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization. New York, NY: IEEE, Institute of Electrical and Electronics Engineers, 2025.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/14070
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/14070
@proceedings{handle:20.500.11811/14070,
author = {{Svetlana Schmidt} and {Linda Klasen} and {Ute Nöthlings} and {Rafet Sifa}},
title = {Towards Automated Recipe Reconstruction : Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization},
publisher = {IEEE, Institute of Electrical and Electronics Engineers},
year = 2025,
month = dec,
note = {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.
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.
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.},
url = {https://hdl.handle.net/20.500.11811/14070}
}
author = {{Svetlana Schmidt} and {Linda Klasen} and {Ute Nöthlings} and {Rafet Sifa}},
title = {Towards Automated Recipe Reconstruction : Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization},
publisher = {IEEE, Institute of Electrical and Electronics Engineers},
year = 2025,
month = dec,
note = {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.
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.
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.},
url = {https://hdl.handle.net/20.500.11811/14070}
}
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