KitcheNette: Predicting and Ranking Food Ingredient Pairings
using Siamese Neural Networks
Abstract
As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been
adopted by chefs and studied by food researchers. In
this work, we propose KitcheNette which is a model
that predicts food ingredient pairing scores and recommends optimal ingredient pairings. KitcheNette
employs Siamese neural networks and is trained
on our annotated dataset containing 300K scores
of pairings generated from numerous ingredients in
food recipes. As the results demonstrate, our model
not only outperforms other baseline models, but also
can recommend complementary food pairings and
discover novel ingredient pairings