| title | PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems |
|---|---|
| authors | Chong Zhang, Qijie Shen, Haichao Zhang, Can Wang, Xiaobo Jin, Jia Wang |
| date | 2026-09-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.13989 |
| affiliation | University of Liverpool |
| upvotes | 9 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 dynamics through its Psy-Engine. This engine represents each user with three evolving, model-defined latent states. To calibrate simulated behavior against real interaction logs, we align log-conditioned reference trajectories with simulator-generated latent-state trajectories via Unbalanced Gromov--Wasserstein optimal transport. This approach provides structured supervision for a lightweight policy without the computational burden of fine-tuning the backbone LLM. We also introduce WebSim and the PsyBer Benchmark, which encompass multimodal scenarios and seven recommender backbones. Our framework evaluates behavioral fidelity, latent-state consistency, robustness, and responses to social-cue perturbations. On the tested horizons, PsyBer-Agent shows lower behavioral discrepancy than the prompt-only simulators while maintaining stable performance under large-scale concurrent simulation for up to 5M queued simulation tasks under bounded concurrency. Code is available in the supplementary material and at: PsyBer-Agent: https://anonymous.4open.science/r/CC66F/, WebSim Platform: https://anonymous.4open.science/r/D8EAX/. |
cite
@misc{zhang2026psyber,
title={PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems},
author={Chong Zhang and Qijie Shen and Haichao Zhang and Can Wang and Xiaobo Jin and Jia Wang},
year={2026},
eprint={2610.13989},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.13989}
}