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

05/16/2019 ∙ by Donghyeon Park, et al. ∙ 0 ∙ share

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.

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

Authors

Deep Learning for Classifying Food Waste

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Food Recognition using Fusion of Classifiers based on CNNs

Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Allocation in Practice

Honey Authentication with Machine Learning Augmented Bright-Field Microscopy

Code Repositories

KitcheNette

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

Authors

Deep Learning for Classifying Food Waste

FoodNet: Recognizing Foods Using Ensemble of Deep Networks

Food Recognition using Fusion of Classifiers based on CNNs

Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

Allocation in Practice

Honey Authentication with Machine Learning Augmented Bright-Field Microscopy

Code Repositories

KitcheNette

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