ArticleScientific reports2021
FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 47 citations in OpenAlex.
- Artificial intelligence for food innovation.Nature food · 2026Review
- From Cookbooks to Networks: A Framework for Comparing Multiethnic Ingredient Systems in Transylvania.Foods (Basel, Switzerland) · 2026Article
- A unified knowledge graph linking foodomics to chemical-disease networks and flavor profiles.NPJ science of food · 2026Article
- Research information systems and knowledge graphs: a review.Frontiers in research metrics and analytics · 2026Review
- Predicting Temporal Liking of Food Pairings from Temporal Dominance of Sensations Data via Reservoir Computing on Crackers and Spreads.Foods (Basel, Switzerland) · 2025Article
- Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence.Food science & nutrition · 2025Review
- Decoding Global Palates: Unveiling Cross-Cultural Flavor Preferences Through Online Recipes.Foods (Basel, Switzerland) · 2025Article
- An Online Multimodal Food Data Exploration Platform for Specific Population Health: Development Study.JMIR formative research · 2024Article
- Review
- Generating Flavor Molecules Using Scientific Machine Learning.ACS omega · 2023Article
- Applications of knowledge graphs for food science and industry.Patterns (New York, N.Y.) · 2022Review
- Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review.Frontiers in nutrition · 2022Review
- Recipe Recommendation With Hierarchical Graph Attention Network.Frontiers in big data · 2021Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Food pairing has not yet been fully pioneered, despite our everyday experience with food and the large amount of food data available on the web. The complementary food pairings discovered thus far created by the intuition of talented chefs, not by scientific knowledge or statistical learning. We introduce FlavorGraph which is a large-scale food graph by relations extracted from million food recipes and information of 1,561 flavor molecules from food databases. We analyze the chemical and statistical relations of FlavorGraph and apply our graph embedding method to better represent foods in dense vectors. Our graph embedding method is a modification of metapath2vec with an additional chemical property learning layer and quantitatively outperforms other baseline methods in food clustering. Food pairing suggestions made based on the food representations of FlavorGraph help achieve better results than previous works, and the suggestions can also be used to predict relations between compounds and foods. Our research offers a new perspective on not only food pairing techniques but also food science in general.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.