An LLM–MCDM Framework with Lin’s Concordance Correlation Coefficient for Recommendation Systems: A Case Study in Food Preference

Food recommender systems are pivotal in helping people make optimal dietary choices based on tremendous amounts of data. Extant studies offer different methods and techniques, but the combination of similarity search, large language models (LLMs), and multi-criteria decision-making (MCDM) remains underexplored. This study proposes a new system that leverages all three. First, we utilize an LLM to suggest queries from the same domain as the dish database. Then, the queries are vectorized and used for similarity search to generate a preliminary list of suggested menu items. Next, multiple LLMs provide scores for each item, which become the MCDM inputs, where Lin’s concordance correlation coefficient (LCCC) enhances the weighted sum scalarization technique. We evaluated the prototype on three publicly available dish datasets and at classification thresholds of 0.25, 0.50, and 0.75, and the proposed domain-adaptation approach consistently outperformed the baseline query. For example, at the 0.50 threshold, precision ranged from 49.11% to 56.60%, compared with 35.40% for the baseline. Furthermore, aggregating multiple LLMs mitigates single-model bias in recommendations. To substantiate this, a bootstrap evaluation of the proposed LCCC-based consensus weighting confirms that both the estimated weights and the induced rankings are numerically stable under sampling perturbations. To further ensure the robustness and reliability of the proposed system, we validate the results against other established weighting schemes and state-of-the-art MCDM methods. Moreover, Kendall’s $\tau$-based comparisons across weighting schemes and multiple MCDM methods confirm that the proposed LCCC-based framework produces highly consistent and statistically significant rankings, demonstrating strong robustness to methodological choices. This paper contributes a system architecture and design that can be adopted for other domains of recommender systems where the capability of multiple LLMs can benefit complex and multifaceted decision-making processes.