构建可动态交互的推荐系统仿真平台，真实模拟用户与商家的实时互动。
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems
- 用户行为实时影响物品属性，商家也能响应，形成动态生态。
- 成功复现品牌忠诚与马太效应等真实系统现象，验证仿真可信度。
- 适合研究推荐系统演化、算法测试与动态环境评估的学者使用。
评估和迭代推荐系统至关重要，但传统A/B测试成本高，离线方法难以捕捉用户与平台的动态交互。尽管基于代理的仿真具有潜力，现有平台常缺乏用户行为动态重塑环境的机制。为此，我们提出RecInter——一个具备强大交互机制的推荐系统代理仿真平台。在该平台中，用户行为（如点赞、评论、购买）可实时更新物品属性，引入的商家代理能作出回应，从而构建更真实、持续演化的生态系统。通过多维用户画像模块、高级代理架构以及在思维链（CoT）增强交互数据上微调的大型语言模型，确保高保真仿真。实验表明，该交互机制对模拟真实系统演化至关重要，平台成功复现了品牌忠诚与马太效应等涌现现象，确立了其作为推荐系统研究可信测试床的地位。代码已开源：https://github.com/jinsong8/RecInter。
原文摘要 · Abstract (English)
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. Our codes are available at https://github.com/jinsong8/RecInter.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。