FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings

Abstract Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learnin...

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Autores principales:	Donghyeon Park, Keonwoo Kim, Seoyoon Kim, Michael Spranger, Jaewoo Kang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d244bc59e1dd43bd8adf781c3aed2da9
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id	oai:doaj.org-article:d244bc59e1dd43bd8adf781c3aed2da9
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spelling	oai:doaj.org-article:d244bc59e1dd43bd8adf781c3aed2da92021-12-02T15:22:59ZFlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings10.1038/s41598-020-79422-82045-2322https://doaj.org/article/d244bc59e1dd43bd8adf781c3aed2da92021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79422-8https://doaj.org/toc/2045-2322Abstract Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.Donghyeon ParkKeonwoo KimSeoyoon KimMichael SprangerJaewoo KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Donghyeon Park Keonwoo Kim Seoyoon Kim Michael Spranger Jaewoo Kang FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
description	Abstract Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.
format	article
author	Donghyeon Park Keonwoo Kim Seoyoon Kim Michael Spranger Jaewoo Kang
author_facet	Donghyeon Park Keonwoo Kim Seoyoon Kim Michael Spranger Jaewoo Kang
author_sort	Donghyeon Park
title	FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
title_short	FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
title_full	FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
title_fullStr	FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
title_full_unstemmed	FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
title_sort	flavorgraph: a large-scale food-chemical graph for generating food representations and recommending food pairings
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d244bc59e1dd43bd8adf781c3aed2da9
work_keys_str_mv	AT donghyeonpark flavorgraphalargescalefoodchemicalgraphforgeneratingfoodrepresentationsandrecommendingfoodpairings AT keonwookim flavorgraphalargescalefoodchemicalgraphforgeneratingfoodrepresentationsandrecommendingfoodpairings AT seoyoonkim flavorgraphalargescalefoodchemicalgraphforgeneratingfoodrepresentationsandrecommendingfoodpairings AT michaelspranger flavorgraphalargescalefoodchemicalgraphforgeneratingfoodrepresentationsandrecommendingfoodpairings AT jaewookang flavorgraphalargescalefoodchemicalgraphforgeneratingfoodrepresentationsandrecommendingfoodpairings
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FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings

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