发表机构
Arizona State University; Applied Materials; University of Illinois Chicago（亚利桑那州立大学; 应用材料公司; 芝加哥大学伊利诺伊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有表格学习方法难以捕获食谱数据分层与序列依赖的问题，提出分层Transformer架构RecipeNet，在多类食谱任务上性能优于现有表格模型，为食谱表示学习提供了新方案。
AI 中文摘要
食谱数据出现在材料合成、药物配方、工业制造等领域，其流程由包含异构结构化字段的有序步骤序列表示。现有表格学习方法通常将该结构展平为固定模式表示，限制了其捕获分层字段交互和流程依赖关系的能力。我们提出RecipeNet，一种分层Transformer架构，通过堆叠的Transformer编码器编码每个步骤内的字段级交互以及步骤间的序列依赖关系。在多个食谱数据集和任务上的实验表明，RecipeNet始终优于现有表格模型，凸显了分层和序列建模对食谱表示学习的价值。
英文摘要
Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.
CommentsAccepted at CIKM 2026
