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 proceedingConference contribution

    2 Citations (Scopus)

    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 languageEnglish
    Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    EditorsSarit Kraus
    PublisherInternational Joint Conferences on Artificial Intelligence
    Pages5930-5936
    Number of pages7
    ISBN (Electronic)9780999241141
    Publication statusPublished - 2019
    Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
    Duration: 2019 Aug 102019 Aug 16

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    Volume2019-August
    ISSN (Print)1045-0823

    Conference

    Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    Country/TerritoryChina
    CityMacao
    Period19/8/1019/8/16

    ASJC Scopus subject areas

    • Artificial Intelligence

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