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

arXiv: https://arxiv.org/abs/2205.11231 · HTML: https://mlsanity.com/paper/2205.11231/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 5 of 5)

- [Adaptive In-Context Learning with Large Language Models for Bundle Generation](https://mlsanity.com/paper/2312.16262/) (2023)
- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Fine-grained Graph Rationalization](https://mlsanity.com/paper/2312.07859/) (2023)
- [AdaptGOT: A Pre-trained Model for Adaptive Contextual POI Representation Learning](https://mlsanity.com/paper/2506.21612/) (2025)
- [Non-autoregressive Personalized Bundle Generation](https://mlsanity.com/paper/2406.06925/) (2024)

## Abstract (arXiv)

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.