用多语言食谱训练食材嵌入，揭示食材间化学与搭配的几何关系。
Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings
- 从414万份跨语言食谱中提取1790个标准食材，构建双图结构
- 三种模型分别聚焦搭配、成分化学、混合关系，精度提升12.6%~18.3%
- 适合食品科学、智能餐饮和风味设计研究者
我们提出Epicure，一套从头训练的三兄弟跳字食材嵌入模型，基于涵盖七种语言（英语、中文、俄语、越南语、西班牙语、土耳其语、印尼语、德语及印式英语）的11个来源共414万份食谱。通过大语言模型增强的流水线，将原始食材字符串归一化为1790个标准条目。构建了包含203,508条边的食材-食材NPMI图和80,019条边的类型化FlavorDB成分-化合物图，涵盖2,247个分类为15类的类型化化合物节点。三个基于Metapath2Vec变体的模型共享架构与超参数，仅在随机游走策略上不同：Cooc仅遍历共现图，Chem仅遍历类型化化合物路径，Core通过可控注入的食材-食材游走融合两者，使各模型位于化学性与食谱上下文之间的不同位置。
原文摘要 · Abstract (English)
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.
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