Rachel Burman

⊹ The Problem

Food waste and economic inflation are reshaping how people cook — but recipe generation tools in 2023 were blunt instruments, pattern-matching on popularity rather than understanding why ingredients work together. The question I wanted to answer: could AI learn the underlying relationships between ingredients well enough to suggest combinations nobody had thought of yet?

⊹ The Approach

I built a knowledge graph of ingredient relationships in Neo4j, pulling recipe data from AllRecipes via the Apify API and nutritional data from FooDB. Ingredient embeddings were generated using Word2Vec, then a GraphSAGE neural network was trained on the graph structure to learn deeper relational patterns. Dimensionality reduction techniques — PCA, UMAP, and t-SNE — were used to surface clusters and outliers in the embedding space. The results were fed to GPT-3.5 via the OpenAI API to generate novel ingredient pairings and full recipes from the learned relationships.

⊹ Results

• Generated unconventional but chemically plausible pairings — including broccoli and blueberries — that the model identified as relationally close despite rarely appearing together in recipes
• Produced novel recipes from scratch, including Broccoli and Ham Stuffed Eggplant with a Blueberry and Jalapeño Glaze — which is either inspired or unhinged, possibly both
• Identified a measurable Western bias in the RecipeNLG dataset, flagging a broader issue with diversity in culinary training data
• Demonstrated a viable end-to-end pipeline from raw recipe data to generative AI output via a graph neural network