Personalized food recommendation can promote healthier, sustainable eating, but current systems often rely on sparse and unstructured data, limiting semantic expressiveness and diverse personalization. In this paper, we propose FoodNexus, a large-scale knowledge graph with nearly one billion triples designed to enrich food recommendation with structured, nutrition-aware, and user-contextual information. We built it via a multi-stage pipeline that combines and augments the largest public dataset of user–recipe interactions, HUMMUS, with extensive metadata from Open Food Facts by linking recipes to concrete food products, extracting user traits from their biographies and reviews, and mapping both data sources onto the same ontology. Experiments show that FoodNexus enables richer, nutrition-sensitive evaluation of recommendations. Code & Resource: https://github.com/tail-unica/food-nexus .