Enhancing LLMs Contextual Knowledge with Ontologies for Personalised Food Recommendation

Food recommendation systems help consumers make sustainable and nutritionally complete choices, promoting healthy eating habits and addressing the growing interest in food sustainability and waste reduction. Large Language Models (LLMs), such as ChatGPT, are increasingly used for food recommendations due to their natural language processing capabilities. However, providing personalised and contextually relevant suggestions remains challenging because of the lack of a robust conceptualisation of healthy and sustainable food aligned with users’ dietary and lifestyle preferences. Ontologies can address this by offering a structured and semantically rich framework for organising information. In this paper, we propose a modular ontology to enhance the contextual knowledge of LLMs, enabling them to deliver personalised, contextually relevant food recommendations. The ontology’s modules are based on competency questions derived from a research project focused on sustainable and healthy food recommendations. To evaluate the effectiveness of this approach, we conducted experiments where ChatGPT-4 answered these competency questions with and without ontology integration. The answers were then assessed in a user study. Preliminary experimental results indicate significant improvements in the quality and relevance of recommendations when the ontology is employed.