Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
This work addresses the need for effective recipe embeddings in food studies, though it appears incremental as it builds on existing graph neural network methods applied to a new domain-specific dataset.
The paper tackles the problem of learning multi-modal recipe representations by integrating visual, textual, and relational information, resulting in Recipe2Vec, which outperforms state-of-the-art baselines on cuisine category classification and region prediction tasks.
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.