Bundle Recommendation with Graph Convolutional Networks
This addresses the problem of recommending bundles of items to users in e-commerce or content platforms, offering an incremental improvement over existing methods.
The paper tackles bundle recommendation by proposing BGCN, a graph neural network that unifies user-item, user-bundle, and bundle-item interactions into a heterogeneous graph, achieving performance gains of 10.77% to 23.18% over state-of-the-art baselines on real-world datasets.
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\%.