Abstract
Learning effective recipe representations is essential in food studies.
Unlike what has been developed for image-based recipe retrieval or learning
structural text embeddings, the combined effect of multi-modal information
(i.e., recipe images, text, and relation data) receives less attention. In this
paper, we formalize the problem of multi-modal recipe representation learning
to integrate the visual, textual, and relational information into recipe
embeddings. In particular, we first present Large-RG, a new recipe graph data
with over half a million nodes, making it the largest recipe graph to date. We
then propose Recipe2Vec, a novel graph neural network based recipe embedding
model to capture multi-modal information. Additionally, we introduce an
adversarial attack strategy to ensure stable learning and improve performance.
Finally, we design a joint objective function of node classification and
adversarial learning to optimize the model. Extensive experiments demonstrate
that Recipe2Vec outperforms state-of-the-art baselines on two classic food
study tasks, i.e., cuisine category classification and region prediction.
Dataset and codes are available at https://github.com/meettyj/Recipe2Vec.