PsyBer-Agent：心理学驱动的推荐系统用户模拟器
原标题：PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems
AI 摘要
研究者提出 PsyBer-Agent，一个心理学驱动的用户模拟器，通过 Psy-Engine 建模认知、情感/社会满意度与自我一致性三个演化隐状态，用于推荐系统部署前评估。该方法利用非平衡 Gromov-Wasserstein 最优传输将离线日志轨迹对齐为轻量在线策略的监督信号，无需微调骨干 LLM。作者同时发布 WebSim 平台与 PsyBer Benchmark，覆盖多模态场景和七种推荐骨干，在测试范围内行为偏差低于纯提示模拟器，并支持最多 500 万排队模拟任务的大规模并发。
正文节选
PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems Abstract Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-based agents generally function as surface imitators that generate realistic short-term responses but fail to capture the underlying user dynamics such as fatigue, affect, and shifting preferences. We propose PsyBer-Agent, a psychology-inspired simulator that models these dyn