A Multinutrient Clustering Framework for Personalized Food Recommendations

Abstract

Personalized nutrition recommendations encounter difficulties in aligning users′ dietary preferences with appropriate foods in extensive nutritional databases comprising thousands of products. This research introduces a multinutrient clustering framework that examines 8790 foods from the USDA National Nutrient Database for Standard Reference, Release 28, utilizing 23 nutritional attributes, including macronutrients, vitamins, and minerals. We thoroughly analyze K‐means and agglomerative clustering algorithms across various configurations (k = 2 − 8), finding that K‐means with eight clusters yields optimal performance, achieving a silhouette score of 0.273 and semantically interpretable dietary categories. The suggested technique shows a 46.4% improvement over suggestions based on popularity and a 273.0% improvement over recommendations based on a single nutrient. This was shown by a thorough evaluation utilizing Precision@K, NDCG, and fivefold cross‐validation (mean silhouette 0.265 ± 0.006). Users can set their own dietary preferences using nutrient sliders, six preset configurations (high protein, low carb, high fiber, low sodium, high calcium, and low calorie), and three configurable weighting strategies (equal, prioritized 3×, and focus‐only). The system then gives them real‐time recommendations (in less than 2 s) with clear similarity scores. A systematic examination with 50 automated test questions shows that the system works well in a wide range of dietary situations. The huge effect sizes (46.4% and 273.0% improvements) suggest that the results are statistically significant. This research connects computational nutrition studies with real‐world dietary advice. It offers an open‐source, understandable system for evidence‐based meal planning that fills important holes in current prediction and classification methods by allowing personalized, multinutrient food suggestions with clear reasoning behind the choices.