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arXiv — NLP / Computation & Language · · 3 min read

Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

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Computer Science > Artificial Intelligence

arXiv:2605.22391 (cs)
[Submitted on 21 May 2026]

Title:Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

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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)
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