# Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation

arXiv: https://arxiv.org/abs/2103.13506 · HTML: https://mlsanity.com/paper/2103.13506/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 17 of 17)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Embedding in Recommender Systems: A Survey](https://mlsanity.com/paper/2310.18608/) (2023)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings](https://mlsanity.com/paper/2212.14077/) (2022)
- [Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs](https://mlsanity.com/paper/2307.03411/) (2023)
- [Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08050/) (2022)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)
- [Learning the effective order of a hypergraph dynamical system](https://mlsanity.com/paper/2306.01813/) (2023)
- [Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation](https://mlsanity.com/paper/2502.09050/) (2025)
- [Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems](https://mlsanity.com/paper/2503.10421/) (2025)
- [Price-guided user attention in large-scale E-commerce group recommendation](https://mlsanity.com/paper/2410.02074/) (2024)
- [Self-supervised Graph Learning for Occasional Group Recommendation](https://mlsanity.com/paper/2112.02274/) (2021)
- [Multi-Granularity Attention Model for Group Recommendation](https://mlsanity.com/paper/2308.04017/) (2023)
- [Enhanced Influence-aware Group Recommendation for Online Media Propagation](https://mlsanity.com/paper/2507.01616/) (2025)
- [Constraints Matrix Diffusion based Generative Neural Solver for Vehicle Routing Problems](https://mlsanity.com/paper/2603.07568/) (2026)
- [AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation](https://mlsanity.com/paper/2605.10367/) (2026)

## Abstract (arXiv)

In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group members' personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces. In addition, the learned user representation is not accurate due to the sparsity of users' interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which however has the great potential to improve the group representation learning. In this work, we focus on addressing the above challenges in group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a GNN-based representation learning network to enhance the learning of individuals' preferences from their friends' preferences, which provides a solid foundation for learning groups' preferences. To exploit the group similarity to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets, and treat the task of group preference learning as embedding hyperedges in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.