Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings
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-oc...
| Authors | Jakub Radzikowski, Josef Chen |
|---|---|
| Primary category | cs.AI |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
Popularity, last 2 days
0 now. Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- Kaikaku/epicure-coocModel · cited by
- Kaikaku/epicure-coreModel · cited by
- Kaikaku/epicure-chemModel · cited by
- Kaikaku/epicure-corpus-resourcesDataset · cited by
Models and datasets referencing this paper
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.