  Bundle recommendation is an emerging research direction in the recommender
system with the focus on recommending customized bundles of items for users.
Although Graph Neural Networks (GNNs) have been applied in this problem and
achieve superior performance, existing methods underexplore the graph-level GNN
methods, which exhibit great potential in traditional recommender system.
Furthermore, they usually lack the transferability from one domain with
sufficient supervision to another domain which might suffer from the label
scarcity issue. In this work, we propose a subgraph-based Graph Neural Network
model, SUGER, for bundle recommendation to handle these limitations. SUGER
generates heterogeneous subgraphs around the user-bundle pairs, and then maps
those subgraphs to the users' preference predictions via neural relational
graph propagation. Experimental results show that SUGER significantly
outperforms the state-of-the-art baselines in both the basic and the transfer
bundle recommendation problems.
