Recipe Embeddings

Recipe embeddings represent recipes as numerical vectors, enabling computers to understand and process culinary information. Current research focuses on creating robust embeddings by integrating multiple data modalities (images, text, ingredient relationships) using techniques like graph neural networks and transformers, often incorporating adversarial training or self-supervised learning to improve model performance and stability. This work is significant for advancing food informatics, facilitating tasks such as recipe retrieval, cuisine classification, and even generating recipes from images, with applications ranging from personalized nutrition recommendations to automated cooking assistants.