# AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation

arXiv: https://arxiv.org/abs/2605.10367 · HTML: https://mlsanity.com/paper/2605.10367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use.
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 11 May 2026.

## Placed in

- LLM agents and tool use / [Agentic recommender systems](https://mlsanity.com/agents/agentic-recommender-systems/)

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Representation Learning with Large Language Models for Recommendation](https://mlsanity.com/paper/2310.15950/) (2023)
- [AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems](https://mlsanity.com/paper/2310.09233/) (2023)
- [CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation](https://mlsanity.com/paper/2310.19488/) (2023)
- [Can LLM be a Personalized Judge?](https://mlsanity.com/paper/2406.11657/) (2024)
- [Collaborative Large Language Model for Recommender Systems](https://mlsanity.com/paper/2311.01343/) (2023)
- [LLM-Powered User Simulator for Recommender System](https://mlsanity.com/paper/2412.16984/) (2024)
- [Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View](https://mlsanity.com/paper/2006.13257/) (2020)
- [CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation](https://mlsanity.com/paper/2206.00242/) (2022)
- [GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation](https://mlsanity.com/paper/2006.03736/) (2020)
- [Large Language Models as Evaluators for Recommendation Explanations](https://mlsanity.com/paper/2406.03248/) (2024)
- [SPRec: Self-Play to Debias LLM-based Recommendation](https://mlsanity.com/paper/2412.09243/) (2024)
- [Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation](https://mlsanity.com/paper/2103.13506/) (2021)
- [An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding](https://mlsanity.com/paper/2406.07138/) (2024)
- [Beyond Utility: Evaluating LLM as Recommender](https://mlsanity.com/paper/2411.00331/) (2024)
- [AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation](https://mlsanity.com/paper/2409.02580/) (2024)
- [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)
- [Unleashing the Power of Large Language Model for Denoising Recommendation](https://mlsanity.com/paper/2502.09058/) (2025)
- [Leveraging Member-Group Relations via Multi-View Graph Filtering for Effective Group Recommendation](https://mlsanity.com/paper/2502.09050/) (2025)
- [Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support](https://mlsanity.com/paper/2507.00535/) (2025)
- [Disentangled Modeling of Preferences and Social Influence for Group Recommendation](https://mlsanity.com/paper/2501.11342/) (2025)

## Abstract (arXiv)

Group Recommendation (GR) aims to suggest items to a group of users, which has become a critical component of modern social platforms. Existing GR methods focus on aggregating individual user preferences with advanced neural networks to infer group preferences. Despite effectiveness, they essentially treat group preference learning as a simple preference aggregation process, failing to capture the complex dynamics of real-world group decision-making. To address these limitations, we propose AgentGR, a novel Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendations, inspired by the semantic reasoning and human behavior simulation capabilities of LLM-driven agents. It aims to jointly capture collaborative-semantic user preferences for member-role-playing and simulate dynamic group interactions to reflect real-world group decision-making processes, thereby boosting recommendation performance. Specifically, to capture collaborative-semantic user preferences, we introduce a semantic meta-path guided chain-of-preference reasoning mechanism that integrates high-order collaborative filtering signals and textual semantics to improve user preference profiles. To model the complex dynamics of group decision-making, we first recognize group topic and leadership to explicitly model the influencing factors within the group decision processes. Building on these, we simulate group-level decision dynamics via two multi-agent simulation strategies for recommendations: a static workflow-based strategy for efficiency and a dynamic dialogue-based strategy for precision. Extensive experiments on two real-world datasets show that AgentGR significantly outperforms state-of-the-art baselines in both recommendation accuracy and group decision simulation, highlighting its potential for real-world GR applications.