Embedding models are used in production for Google Search, in the Discover Weekly recommendation system at Spotify, and for learning representations of biological systems like genes and proteins. In this work, we develop an embedding model for foods based on patterns in a large recipe dataset. A recommendation system for food is built based on the embedding model, and we show that our model learns concepts such as which foods are complementary or which foods can be substituted for each other in recipes. The code and data are open source and readily extendable to new kinds of data.
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