{"type":"ScholarlyArticle","title":"KitcheNette: Predicting and Ranking Food Ingredient Pairings using Siamese Neural Network","authors":[{"name":"Donghyeon Park","slug":"donghyeon-park","given":"Donghyeon","family":"Park","affiliation":"Korea University"},{"name":"Keonwoo Kim","slug":"keonwoo-kim","given":"Keonwoo","family":"Kim","affiliation":"Korea University"},{"name":"Yonggyu Park","slug":"yonggyu-park","given":"Yonggyu","family":"Park","affiliation":"Korea University"},{"name":"Jungwoon Shin","slug":"jungwoon-shin","given":"Jungwoon","family":"Shin","affiliation":"Korea University"},{"name":"Jaewoo Kang","slug":"jaewoo-kang","given":"Jaewoo","family":"Kang","affiliation":"Korea University"}],"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.","doi":"10.24963/ijcai.2019/822","url":"https://ijcai.org/proceedings/2019/822","pdf":"https://ijcai.org/proceedings/2019/0822.pdf","markdown":"https://ijcai.org/proceedings/2019/822.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/822","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":822,"pages":{"first":5930,"last":5936},"keywords":["Special Track on AI for Improving Human-Well Being: AI benefits to society AI applications (Special Track on AI and Human Wellbeing)","Special Track on AI for Improving Human-Well Being: AI applications for Improving Human-Well Being (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Ahn and Ahnert, 2013] Yong-Yeol Ahn and Sebastian Ahn- ert. 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