Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

View PDF HTML (experimental) Abstract:We present Epicure, a family of three sibling skip-gram ingredient embeddings retrained from scratch on a multilingual recipe corpus. We aggregate 4.14M recipes from 11 sources spanning seven languages, English, Chinese, Russian, Vietnamese, Spanish, Turkish, Indonesian, German, and Indian-English, and normalise the raw ingredient strings to 1,790 canonical entries via an LLM-augmented pipeline. A 203,508-edge ingredient-ingredient NPMI graph and an 80,019-edge typed FlavorDB ingredient-compound graph, 2,247 typed compound nodes across 15 categories, seed three Metapath2Vec variants that share architecture and hyperparameters and differ only in the random-walk schema: Cooc walks the co-occurrence graph only, Chem walks the typed compound metapaths only, and Core blends both via injected ingredient-ingredient walks at controlled mixing, placing each model at a distinct point on the chemistry-vs-recipe-context spectrum.

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) Cite as: arXiv:2605.22391 [cs.AI]   (or arXiv:2605.22391v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.22391 arXiv-issued DOI via DataCite (pending registration) Submission history From: Josef Liyanjun Chen [view email] [v1] Thu, 21 May 2026 12:23:38 UTC (6,566 KB)