@inproceedings{seo-2025-flavordiffusion,
    title = "{F}lavor{D}iffusion: Modeling Food-Chemical Interactions with Diffusion",
    author = "Seo, Junpyo  and
      Kim, Dongwan  and
      Jeong, Jaewook  and
      Park, Inkyu  and
      Min, Junho",
    editor = "Jansen, Peter  and
      Dalvi Mishra, Bhavana  and
      Trivedi, Harsh  and
      Prasad Majumder, Bodhisattwa  and
      Hope, Tom  and
      Khot, Tushar  and
      Downey, Doug  and
      Horvitz, Eric",
    booktitle = "Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.aisd-main.7/",
    doi = "10.18653/v1/2025.aisd-main.7",
    pages = "70--77",
    ISBN = "979-8-89176-224-4",
    abstract = "The study of food pairing has evolved beyond subjective expertise with the advent of machine learning. This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interactions and ingredient pairings without relying on chromatography. By integrating graph-based embeddings, diffusion processes, and chemical property encoding, FlavorDiffusion addresses data imbalances and enhances clustering quality. Using a heterogeneous graph derived from datasets like Recipe1M and FlavorDB, our model demonstrates superior performance in reconstructing ingredient-ingredient relationships. The addition of a Chemical Structure Prediction (CSP) layer further refines the embedding space, achieving state-of-the-art NMI scores and enabling meaningful discovery of novel ingredient combinations. The proposed framework represents a significant step forward in computational gastronomy, offering scalable, interpretable, and chemically informed solutions for food science."
}