SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

TL;DR

SUGER introduces a subgraph-based GNN approach for bundle recommendation, leveraging graph-level features and transfer learning to improve performance and address label scarcity.

Contribution

The paper proposes SUGER, a novel subgraph-based GNN model that enhances bundle recommendation and transferability across domains.

Findings

SUGER significantly outperforms state-of-the-art baselines.

Effective in both basic and transfer recommendation tasks.

Addresses label scarcity in cross-domain scenarios.

Abstract

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.