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[ARTICLE · art-15524] src=tomacco.github.io pub= topic=machine-learning verified=true sentiment=· neutral

Show HN: World Flavor Atlas. Radzikowski and Chen's food embeddings in 3D

Radzikowski and Chen released World Flavor Atlas, a 3D interactive map that visualizes 250 food ingredients based on their relationships in recipes and flavor chemistry. The tool uses three embedding models—COOC, CORE, and CHEM—to show how ingredients cluster differently depending on whether they co-occur in recipes or share molecular flavor compounds.

read1 min publishedMay 27, 2026

Navigating the Geometry of Food

SLERP Journey

You're looking at 250 food ingredients from cuisines around the world, mapped into 3D space by how they relate to each other. Ingredients that appear together in recipes cluster together. Ingredients that share flavor chemistry pull closer.

Three embedding models — COOC (co-occurrence in recipes), CORE (blended), and CHEM (flavor chemistry) — each organize the same ingredients differently. Drag the slider to watch the clusters reshape in real time.

source & further reading
tomacco.github.io — original article
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