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New embeddings map food ingredient relationships using recipes and chemistry

By PulseAugur Editorial · [2 sources] ·

Researchers have developed "Epicure," a set of three skip-gram embeddings trained on a large multilingual recipe corpus. These embeddings are designed to capture the relationships between food ingredients, considering both co-occurrence in recipes and chemical compound data. The models, named Cooc, Chem, and Core, offer different balances between recipe context and chemical properties, providing a nuanced understanding of ingredient interactions. AI

IMPACT Introduces novel embeddings for food ingredients, potentially enabling new applications in recipe generation and food science.

RANK_REASON The cluster contains an academic paper detailing a new method for creating embeddings.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New embeddings map food ingredient relationships using recipes and chemistry

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The cluster contains an academic paper detailing a new method for creating embeddings.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jakub Radzikowski, Josef Chen ·

    Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

    arXiv:2605.22391v1 Announce Type: cross 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, …

  2. arXiv cs.AI TIER_1 English(EN) · Josef Chen ·

    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, …

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