Abstract
Recipe recommendation systems play an essential role in helping people decide
what to eat. Existing recipe recommendation systems typically focused on
content-based or collaborative filtering approaches, ignoring the higher-order
collaborative signal such as relational structure information among users,
recipes and food items. In this paper, we formalize the problem of recipe
recommendation with graphs to incorporate the collaborative signal into recipe
recommendation through graph modeling. In particular, we first present
URI-Graph, a new and large-scale user-recipe-ingredient graph. We then propose
RecipeRec, a novel heterogeneous graph learning model for recipe
recommendation. The proposed model can capture recipe content and collaborative
signal through a heterogeneous graph neural network with hierarchical attention
and an ingredient set transformer. We also introduce a graph contrastive
augmentation strategy to extract informative graph knowledge in a
self-supervised manner. Finally, we design a joint objective function of
recommendation and contrastive learning to optimize the model. Extensive
experiments demonstrate that RecipeRec outperforms state-of-the-art methods for
recipe recommendation. Dataset and codes are available at
https://github.com/meettyj/RecipeRec.