  Bundle recommendation aims to recommend a bundle of items for a user to
consume as a whole. Existing solutions integrate user-item interaction modeling
into bundle recommendation by sharing model parameters or learning in a
multi-task manner, which cannot explicitly model the affiliation between items
and bundles, and fail to explore the decision-making when a user chooses
bundles. In this work, we propose a graph neural network model named BGCN
(short for \textit{\textBF{B}undle \textBF{G}raph \textBF{C}onvolutional
\textBF{N}etwork}) for bundle recommendation. BGCN unifies user-item
interaction, user-bundle interaction and bundle-item affiliation into a
heterogeneous graph. With item nodes as the bridge, graph convolutional
propagation between user and bundle nodes makes the learned representations
capture the item level semantics. Through training based on hard-negative
sampler, the user's fine-grained preferences for similar bundles are further
distinguished. Empirical results on two real-world datasets demonstrate the
strong performance gains of BGCN, which outperforms the state-of-the-art
baselines by 10.77\% to 23.18\%.
