Department of Computer Science and Engineering, Korea University, Seoul, 02841, South Korea.
SONY AI, SONY Corporation, Tokyo, 108-0075, Japan.
Sci Rep. 2021 Jan 13;11(1):931. doi: 10.1038/s41598-020-79422-8.
Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.
尽管我们每天都在体验食物，并且网络上也有大量的食物数据，但食物搭配仍未得到充分的探索。迄今为止，通过有天赋的厨师的直觉发现的互补食物搭配，而不是通过科学知识或统计学习。我们引入了 FlavorGraph，这是一个从百万个食谱中提取关系的大规模食物图谱，以及来自食物数据库的 1561 种风味分子的信息。我们分析了 FlavorGraph 的化学和统计关系，并应用我们的图嵌入方法来更好地用密集向量表示食物。我们的图嵌入方法是 metapath2vec 的一种修改，增加了一个化学性质学习层，在食物聚类方面的性能明显优于其他基线方法。基于 FlavorGraph 的食物表示提出的食物搭配建议比以前的工作取得了更好的结果，并且这些建议还可以用于预测化合物和食物之间的关系。我们的研究不仅为食物搭配技术，而且为一般的食物科学提供了新的视角。
