  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.
