# AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation

arXiv: https://arxiv.org/abs/2409.02580 · HTML: https://mlsanity.com/paper/2409.02580/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020)
- [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2209.02544/) (2022)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Adaptive Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2305.10837/) (2023)
- [Overcoming Data Sparsity in Group Recommendation](https://mlsanity.com/paper/2010.00813/) (2020)
- [GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation](https://mlsanity.com/paper/2006.03736/) (2020)
- [MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation](https://mlsanity.com/paper/2402.19407/) (2024)
- [Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation](https://mlsanity.com/paper/2103.13506/) (2021)
- [Thinking inside The Box: Learning Hypercube Representations for Group Recommendation](https://mlsanity.com/paper/2204.02592/) (2022)
- [ConsRec: Learning Consensus Behind Interactions for Group Recommendation](https://mlsanity.com/paper/2302.03555/) (2023)
- [Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation](https://mlsanity.com/paper/1804.04327/) (2018)
- [Personality-Driven Social Multimedia Content Recommendation](https://mlsanity.com/paper/2207.12236/) (2022)
- [Learning User Representations with Hypercuboids for Recommender Systems](https://mlsanity.com/paper/2011.05742/) (2020)

## Built on by (most cited first, 13 of 13)

- [Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://mlsanity.com/paper/2508.02853/) (2025)
- [CPDR: Towards Highly-Efficient Salient Object Detection via Crossed Post-decoder Refinement](https://mlsanity.com/paper/2501.06441/) (2025)
- [DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation](https://mlsanity.com/paper/2501.15385/) (2025)
- [CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation](https://mlsanity.com/paper/2501.15389/) (2025)
- [Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning](https://mlsanity.com/paper/2512.08763/) (2025)
- [Leveraging Optimal Transport for Distributed Two-Sample Testing: An Integrated Transportation Distance-based Framework](https://mlsanity.com/paper/2506.16047/) (2025)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [A Local Perspective-based Model for Overlapping Community Detection](https://mlsanity.com/paper/2503.21558/) (2025)
- [Segregation and Context Aggregation Network for Real-time Cloud Segmentation](https://mlsanity.com/paper/2504.14178/) (2025)
- [Stochastic Deep Graph Clustering for Practical Group Formation](https://mlsanity.com/paper/2511.02879/) (2025)
- [Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation](https://mlsanity.com/paper/2512.13734/) (2025)
- [CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.04320/) (2026)
- [AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation](https://mlsanity.com/paper/2605.10367/) (2026)

## Abstract (arXiv)

Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences: 1) determining group preferences by aggregating members' personalized preferences, and 2) inferring group consensus by capturing group members' coherent decisions after common compromises. However, the former would suffer from the lack of group-level considerations, and the latter overlooks the fine-grained preferences of individual users. To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. Specifically, AlignGroup explores group consensus through a well-designed hypergraph neural network that efficiently learns intra- and inter-group relationships. Moreover, AlignGroup innovatively utilizes a self-supervised alignment task to capture fine-grained group decision-making by aligning the group consensus with members' common preferences. Extensive experiments on two real-world datasets validate that our AlignGroup outperforms the state-of-the-art on both the group recommendation task and the user recommendation task, as well as outperforms the efficiency of most baselines.