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    <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.</abstract>
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%0 Conference Proceedings
%T FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion
%A Seo, Junpyo
%A Kim, Dongwan
%A Jeong, Jaewook
%A Park, Inkyu
%A Min, Junho
%Y Jansen, Peter
%Y Dalvi Mishra, Bhavana
%Y Trivedi, Harsh
%Y Prasad Majumder, Bodhisattwa
%Y Hope, Tom
%Y Khot, Tushar
%Y Downey, Doug
%Y Horvitz, Eric
%S Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-224-4
%F seo-2025-flavordiffusion
%X 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.
%R 10.18653/v1/2025.aisd-main.7
%U https://aclanthology.org/2025.aisd-main.7/
%U https://doi.org/10.18653/v1/2025.aisd-main.7
%P 70-77

Markdown (Informal)

[FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion](https://aclanthology.org/2025.aisd-main.7/) (Seo et al., AISD 2025)

• FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion (Seo et al., AISD 2025)

ACL

• Junpyo Seo, Dongwan Kim, Jaewook Jeong, Inkyu Park, and Junho Min. 2025. FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion. In Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities, pages 70–77, Albuquerque, New Mexico, USA. Association for Computational Linguistics.