Exploring Epicure the Food Embedding Model

FlavorGraph is a large-scale graph network that combines data from over a million recipes with chemical compound information from 1,500+ flavor molecules to predict ingredient pairings. It uses graph embedding methods to represent foods as dense vectors, enabling data-driven food pairing suggestions that go beyond human or chef intuition. In FlavorGraph, the chemical and recipe context signals are fused at training time via a fixed metapath design, leaving no inference-time knob to adjust their relative weights in the final embeddings. One could call Epicure an enhanced FlavorGraph. It builds on FlavorGraph to produce 300-D embeddings, but instead of a single embedding that combines both chemical and recipe context signals. It has three embedding models Cooc, Chem, and Core. That way, as a user, you can choose the embedding you want. It also includes more recipes from other languages, not just English. Recipes from English, Chinese, Russian, Vietnamese, Spanish, Turkish, Indonesian,…