# GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

arXiv: https://arxiv.org/abs/2006.03736 · HTML: https://mlsanity.com/paper/2006.03736/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 21 of 21)

- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph](https://mlsanity.com/paper/2211.01830/) (2022)
- [Accurate Bundle Matching and Generation via Multitask Learning with Partially Shared Parameters](https://mlsanity.com/paper/2210.15460/) (2022)
- [Decentralized Graph Neural Network for Privacy-Preserving Recommendation](https://mlsanity.com/paper/2308.08072/) (2023)
- [Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation](https://mlsanity.com/paper/2502.09050/) (2025)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)
- [Price-guided user attention in large-scale E-commerce group recommendation](https://mlsanity.com/paper/2410.02074/) (2024)
- [FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences](https://mlsanity.com/paper/2504.09428/) (2025)
- [DeepGroup: Representation Learning for Group Recommendation with Implicit Feedback](https://mlsanity.com/paper/2103.07597/) (2021)
- [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)
- [UNICON: A unified framework for behavior-based consumer segmentation in e-commerce](https://mlsanity.com/paper/2309.13068/) (2023)
- [Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2311.02938/) (2023)
- [Treatment Effect Estimation for Graph-Structured Targets](https://mlsanity.com/paper/2412.20436/) (2024)
- [Collaborative Interest-aware Graph Learning for Group Identification](https://mlsanity.com/paper/2506.14826/) (2025)
- [Enhanced Influence-aware Group Recommendation for Online Media Propagation](https://mlsanity.com/paper/2507.01616/) (2025)
- [Stochastic Deep Graph Clustering for Practical Group Formation](https://mlsanity.com/paper/2511.02879/) (2025)
- [DALI: LLM-Agent Enhanced Dual-Stream Adaptive Leadership Identification for Group Recommendations](https://mlsanity.com/paper/2603.19909/) (2026)
- [AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation](https://mlsanity.com/paper/2605.10367/) (2026)

## Abstract (arXiv)

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group. We make two contributions. First, we present a recommender architecture-agnostic framework GroupIM that can integrate arbitrary neural preference encoders and aggregators for ephemeral group recommendation. Second, we regularize the user-group latent space to overcome group interaction sparsity by: maximizing mutual information between representations of groups and group members; and dynamically prioritizing the preferences of highly informative members through contextual preference weighting. Our experimental results on several real-world datasets indicate significant performance improvements (31-62% relative NDCG@20) over state-of-the-art group recommendation techniques.