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.
View on arXiv@article{jin2025_2505.16429,
title={ Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems },
author={ Song Jin and Juntian Zhang and Yuhan Liu and Xun Zhang and Yufei Zhang and Guojun Yin and Fei Jiang and Wei Lin and Rui Yan },
journal={arXiv preprint arXiv:2505.16429},
year={ 2025 }
}