PsyBer-Agent simulates user fatigue, affect and shifting preferences for recommender testing

Key points

• Paper proposes PsyBer-Agent, a psychology-inspired simulator whose Psy-Engine tracks three evolving latent states per user.
• Calibrates simulated behavior to real interaction logs using Unbalanced Gromov-Wasserstein optimal transport, without fine-tuning the backbone LLM.
• Reports lower behavioral discrepancy than prompt-only simulators on tested horizons, and stable performance at up to 5M queued tasks.
• Introduces WebSim and the PsyBer Benchmark, covering multimodal scenarios and seven recommender backbones; code is linked in the paper.

Why it matters for marketers: Teams testing recommenders before launch may get simulated users that hold up over long interaction cycles, not just short realistic replies. This is an arXiv preprint, not peer reviewed.