@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://aclanthology.org/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."
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<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 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 Merchant Agents can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through Multidimensional User Profiling module, 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 Brand Loyalty and the 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.</abstract>
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%0 Conference Proceedings
%T Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
%A Jin, Song
%A Zhang, Juntian
%A Liu, Yuhan
%A Zhang, Xun
%A Zhang, Yufei
%A Yin, Guojun
%A Jiang, Fei
%A Lin, Wei
%A Yan, Rui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F jin-etal-2025-beyond
%X 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 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 Merchant Agents can reply, fostering a more realistic and evolving ecosystem. High-fidelity simulation is ensured through Multidimensional User Profiling module, 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 Brand Loyalty and the 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.
%U https://aclanthology.org/2025.emnlp-main.956/
%P 18914-18931
Markdown (Informal)
[Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems](https://aclanthology.org/2025.emnlp-main.956/) (Jin et al., EMNLP 2025)
ACL
- Song Jin, Juntian Zhang, Yuhan Liu, Xun Zhang, Yufei Zhang, Guojun Yin, Fei Jiang, Wei Lin, and Rui Yan. 2025. Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 18914–18931, Suzhou, China. Association for Computational Linguistics.