KitcheNette: Predicting and Ranking Food Ingredient Pairings Using Siamese Neural Network

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

IJCAI 2019 pp. 5930-5936

doi:10.24963/IJCAI.2019/822 /ijcai/2019/park2019ijcai-kitchenette/

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.

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Cite

Text

Park et al. "KitcheNette: Predicting and Ranking Food Ingredient Pairings Using Siamese Neural Network." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/822

Markdown

[Park et al. "KitcheNette: Predicting and Ranking Food Ingredient Pairings Using Siamese Neural Network." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/park2019ijcai-kitchenette/) doi:10.24963/IJCAI.2019/822

BibTeX

@inproceedings{park2019ijcai-kitchenette,
  title     = {{KitcheNette: Predicting and Ranking Food Ingredient Pairings Using Siamese Neural Network}},
  author    = {Park, Donghyeon and Kim, Keonwoo and Park, Yonggyu and Shin, Jungwoon and Kang, Jaewoo},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {5930-5936},
  doi       = {10.24963/IJCAI.2019/822},
  url       = {https://mlanthology.org/ijcai/2019/park2019ijcai-kitchenette/}
}