Recipe is a set of instructions that describes how to make food. It can help people from the preparation of ingredients, food cooking process, etc. to prepare the food, and increasingly in demand on the Web. To help users find the vast amount of recipes on the Web, we address the task of recipe recommendation. Due to multiple data types and relationships in a recipe, we can treat it as a heterogeneous network to describe its information more accurately. To effectively utilize the heterogeneous network, metapath was proposed to describe the higher-level semantic information between two entities by defining a compound path from peer entities. Therefore, we propose a metapath-enhanced recipe recommendation framework, RecipeMeta, that combines GNN (Graph Neural Network)-based representation learning and specific metapath-based information in a recipe to predict User-Recipe pairs for recommendation. Through extensive experiments, we demonstrate that the proposed model, RecipeMeta, outperforms state-of-the-art methods for recipe recommendation.

翻译：食谱是一套描述如何制作食物的指令，它能帮助人们从食材准备、烹饪过程等环节完成食物制作，且在互联网上的需求日益增长。为帮助用户在海量网络食谱中高效检索，本文聚焦食谱推荐任务。由于食谱包含多种数据类型与关联关系，可将其建模为异质网络以更精准地描述其信息。为有效利用异质网络，元路径被提出用于通过定义实体间的复合路径来描述更高层的语义信息。为此，我们提出一种元路径增强的食谱推荐框架RecipeMeta，该框架结合基于图神经网络（GNN）的表征学习与食谱中特定元路径的信息，对用户-食谱对进行推荐预测。通过大量实验证明，所提出的RecipeMeta模型在食谱推荐任务上优于现有最优方法。