# KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks

## TL;DR

KitcheNette is a Siamese neural network-based model that predicts and recommends food ingredient pairings, outperforming baselines and discovering novel combinations from a large recipe dataset.

## Contribution

The paper introduces KitcheNette, a novel neural network model for predicting and recommending ingredient pairings using a large annotated dataset.

## Key findings

- Outperforms baseline models in pairing prediction
- Can recommend complementary ingredient pairings
- Discovers novel ingredient combinations

## 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.