Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks

Donghyeon Park; Keonwoo Kim; Yonggyu Park; Jungwoon Shin; Jaewoo Kang

Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks

Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, Jaewoo Kang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Editors	Sarit Kraus
Publisher	International Joint Conferences on Artificial Intelligence
Pages	5930-5936
Number of pages	7
ISBN (Electronic)	9780999241141
Publication status	Published - 2019
Event	28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China Duration: 2019 Aug 10 → 2019 Aug 16

Publication series

Name	IJCAI International Joint Conference on Artificial Intelligence
Volume	2019-August
ISSN (Print)	1045-0823

Conference

Conference	28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Country/Territory	China
City	Macao
Period	19/8/10 → 19/8/16

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government of Korea (MSIP) (NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887)

Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

ASJC Scopus subject areas

Artificial Intelligence

Cite this

Park, D., Kim, K., Park, Y., Shin, J., & Kang, J. (2019). Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 5930-5936). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August). International Joint Conferences on Artificial Intelligence.

Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks. / Park, Donghyeon; Kim, Keonwoo; Park, Yonggyu et al.
Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. ed. / Sarit Kraus. International Joint Conferences on Artificial Intelligence, 2019. p. 5930-5936 (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Park, D, Kim, K, Park, Y, Shin, J & Kang, J 2019, Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks. in S Kraus (ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. IJCAI International Joint Conference on Artificial Intelligence, vol. 2019-August, International Joint Conferences on Artificial Intelligence, pp. 5930-5936, 28th International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 19/8/10.

Park D, Kim K, Park Y, Shin J, Kang J. Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks. In Kraus S, editor, Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. International Joint Conferences on Artificial Intelligence. 2019. p. 5930-5936. (IJCAI International Joint Conference on Artificial Intelligence).

Park, Donghyeon ; Kim, Keonwoo ; Park, Yonggyu et al. / Kitchenette : Predicting and ranking food ingredient pairings using siamese neural networks. Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. editor / Sarit Kraus. International Joint Conferences on Artificial Intelligence, 2019. pp. 5930-5936 (IJCAI International Joint Conference on Artificial Intelligence).

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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.",

author = "Donghyeon Park and Keonwoo Kim and Yonggyu Park and Jungwoon Shin and Jaewoo Kang",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the government of Korea (MSIP) (NRF-2017R1A2A1A17069645, NRF-2017M3C4A7065887) Publisher Copyright: {\textcopyright} 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.; 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 ; Conference date: 10-08-2019 Through 16-08-2019",

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Kitchenette: Predicting and ranking food ingredient pairings using siamese neural networks

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

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