AgentGR uses semantic-aware LLM agents to simulate group decision dynamics and improve group recommendation accuracy over traditional aggregation methods.
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PGHS fuses policy-guided LLM reasoning and ML fitting to simulate group user behavior with 8.8% error on Meituan data from 101 merchants and 26k trajectories, beating pure reasoning and fitting baselines by 45.8% and 40.9%.
PDR is a user-context-aware framework for LLM research agents that improves report relevance over static baselines, supported by a new dataset and hybrid evaluation.
citing papers explorer
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AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation
AgentGR uses semantic-aware LLM agents to simulate group decision dynamics and improve group recommendation accuracy over traditional aggregation methods.
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Meituan Merchant Business Diagnosis via Policy-Guided Dual-Process User Simulation
PGHS fuses policy-guided LLM reasoning and ML fitting to simulate group user behavior with 8.8% error on Meituan data from 101 merchants and 26k trajectories, beating pure reasoning and fitting baselines by 45.8% and 40.9%.
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Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
PDR is a user-context-aware framework for LLM research agents that improves report relevance over static baselines, supported by a new dataset and hybrid evaluation.