RecipeRAG: A Knowledge Graph-Driven Approach to Personalized Recipe Retrieval and Generation

Recommending or generating a recipe that satisfies the growing diversity of dietary needs and preferences is a significant challenge for many people. Food choices are influenced by a mix of factors (taste, dietary restriction, health, availability of ingredients), yet many algorithms are optimized for single-criterion decisions. In this work, we introduce RecipeRAG, a novel knowledge graph based retrieval-augmented generation system for personalized recipe generation based on users' multiple criteria. We construct RecipeKG, a knowledge graph derived from Food.com and enriched with additional semantic tags, to capture complex relationships between recipes and their related concepts and use knowledge graph embedding models for retrieval. RecipeRAG employs multi-criteria information retrieval on RecipeKG on user-defined constraints, followed by a large language model to generate personalized recipes. Our experiments demonstrate that RecipeRAG outperforms existing methods in both retrieval and generation tasks, producing high-quality personalized recipes that meet multiple constraints. RecipeRAG offers a promising solution to make a connection between traditional recipes and evolving nutritional and dietary needs, allowing for more flexible and personalized cooking options.