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

2026-05-11

AI Summary

This work addresses the limitations of existing group recommendation approaches, which predominantly rely on static aggregation of individual preferences and fail to capture the dynamic interactions and semantic nuances inherent in real-world group decision-making. To overcome this, the authors propose a large language model–driven multi-agent simulation framework that explicitly models group-level dynamics—including topical focus, leadership roles, and member interactions—through role-playing mechanisms and semantic meta-path–guided preference reasoning. By integrating high-order collaborative filtering, textual semantic analysis, and conversational multi-agent simulation, the proposed method achieves significant improvements over state-of-the-art baselines on two real-world datasets, demonstrating enhanced recommendation accuracy and greater fidelity in simulating authentic group decision processes.

Abstract

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.

Problem

• Group Recommendation
• Group Decision-Making
• Preference Aggregation
• Semantic Reasoning
• Multi-Agent Simulation

Innovation

• Group Recommendation
• LLM-driven Agents
• Semantic Meta-path
• Multi-agent Simulation
• Preference Reasoning