Adaptive Constraint Relaxation in Personalized Nutrition Recommendations: An LLM-Driven Knowledge Graph Retrieval Approach.

Abstract

Personalized food recommendation systems must balance various constraints, including medical guidelines, nutritional needs, and individual preferences. However, existing methods often struggle with overly restrictive queries, frequently failing to generate recommendations when no exact match exists. To address this challenge, we propose an adaptive knowledge graph (KG) retrieval framework that integrates Large Language Models (LLMs) for intelligent constraint relaxation. Our approach dynamically prioritizes constraints, ensuring that critical dietary requirements remain intact while selectively relaxing less essential ones. By leveraging LLM-driven constraint analysis and structured relaxation strategies, our system significantly enhances recommendation coverage without compromising key dietary needs, while maintaining optimal recommendation performance. Experimental results on both the original and the extended-constraint dataset demonstrate that our method successfully retrieves recommendations in cases where previous approaches fail, achieving higher retrieval accuracy and a balanced tradeoff between flexibility and adherence to dietary constraints. The code is public available at https://github.com/zpf0117b2/adaptiveRetrieval.