@inproceedings{jin-etal-2025-beyond,
    title = "Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems",
    author = "Jin, Song  and
      Zhang, Juntian  and
      Liu, Yuhan  and
      Zhang, Xun  and
      Zhang, Yufei  and
      Yin, Guojun  and
      Jiang, Fei  and
      Lin, Wei  and
      Yan, Rui",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.956/",
    pages = "18914--18931",
    ISBN = "979-8-89176-332-6",
    abstract = "Evaluating and iterating upon recommender systems is crucial, yet traditional A/B testing is resource-intensive, and offline methods struggle with dynamic user-platform interactions. While agent-based simulation is promising, existing platforms often lack a mechanism for user actions to dynamically reshape the environment. To bridge this gap, we introduce $\textbf{RecInter }$, a novel agent-based simulation platform for recommender systems featuring a robust interaction mechanism. In RecInter platform, simulated user actions (e.g., likes, reviews, purchases) dynamically update item attributes in real-time, and introduced $\textit{Merchant Agents}$ can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through $\textit{ Multidimensional User Profiling module}$, $\textit{Advanced Agent Architecture}$, and LLM fine-tuned on Chain-of-Thought (CoT) enriched interaction data. Our platform achieves significantly improved simulation credibility and successfully replicates emergent phenomena like $\textit{Brand Loyalty}$ and the $\textit{Matthew Effect}$. Experiments demonstrate that this interaction mechanism is pivotal for simulating realistic system evolution, establishing our platform as a credible testbed for recommender systems research. All codes are released in https://github.com/jinsong8/RecInter."
}