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.

翻译：我们提出Epicure，一个由三个兄弟跳跃元语法（skip-gram）食材嵌入向量组成的系列，该系列在多语言食谱语料库上从头重新训练。我们聚合了来自11个来源、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、德语及印度英语）的414万份食谱，并通过大语言模型增强管道将原始食材字符串归一化为1790个规范词条。一个包含203,508条边的食材-食材NPMI图、一个包含80,019条边的带类型FlavorDB食材-化合物图（含2247个带类型化合物节点，分属15个类别），共同构成了三个Metapath2Vec变体的基础，这些变体共享架构和超参数，仅在随机游走模式上有所不同：Cooc仅遍历共现图，Chem仅遍历带类型化合物元路径，而Core通过受控混合注入的食材-食材游走融合了两种模式，使每个模型位于化学-食谱上下文频谱的不同位置。