This is a short summary of our paper “Towards Automated Recipe Reconstruction: Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization” by Svetlana Schmidt, Linda Klasen, Ute Nöthlings, and Rafet Sifa published in the proceedings of the 2025 IEEE International Conference on Big Data. See here for the full paper.

Expanding nutritional databases for epidemiological research (specifically the NutriDiary system at the University of Bonn) currently relies on manual, labor-intensive recipe matching and simulation by dietitians, a process that is slow and error-prone.

Contributions:

Information Retrieval (IR) Recommender System: We propose a two-stage IR system that combines TF-IDF text similarity (product names + ingredients) with nutrient profile similarity to find matching food items in the database. A key enhancement is food category prediction, which narrows the search space before retrieval. Among the tested classifiers (Logistic Regression, SVM variants, neural networks, BERT, DistilBERT), the polynomial- and RBF-kernel SVCs performed best, achieving 99% and 98.6% accuracy, respectively. Adding category filtering boosted retrieval precision from 73.74% to 80.48%, while maintaining a 92% Success@K across all models.

Recipe Simulation Pipeline (Roadmap): For products that cannot be matched, we outline a hybrid pipeline combining:

• LLMs with Chain-of-Thought prompting for ingredient parsing and normalization
• Mathematical optimization (linear/quadratic programming) to estimate ingredient proportions constrained by declared nutrient values
• Expert-in-the-loop validation by dietitians as a quality gate

Key Findings: Kernel SVMs outperformed transformer models (BERT/DistilBERT) for food categorization, likely because TF-IDF captures domain-specific lexical patterns better than general-purpose contextual embeddings. Annotation inconsistencies in the dataset were also identified as a limiting factor requiring label harmonization.