The implicit feedback (e.g., clicks) in real-world recommender systems is often prone to severe noise caused by unintentional interactions, such as misclicks or curiosity-driven behavior. A common approach to denoising this feedback is manually crafting rules based on observations of training loss patterns. However, this approach is labor-intensive and the resulting rules often lack generalization across diverse scenarios. To overcome these limitations, we introduce RuleAgent, a language agent based framework which mimics real-world data experts to autonomously discover rules for recommendation denoising. Unlike the high-cost process of manual rule mining, RuleAgent offers rapid and dynamic rule discovery, ensuring adaptability to evolving data and varying scenarios. To achieve this, RuleAgent is equipped with tailored profile, memory, planning, and action modules and leverages reflection mechanisms to enhance its reasoning capabilities for rule discovery. Furthermore, to avoid the frequent retraining in rule discovery, we propose LossEraser-an unlearning strategy that streamlines training without compromising denoising performance. Experiments on benchmark datasets demonstrate that, compared with existing denoising methods, RuleAgent not only derives the optimal recommendation performance but also produces generalizable denoising rules, assisting researchers in efficient data cleaning.

翻译：现实推荐系统中的隐式反馈（如点击）常因误点击或好奇驱动行为等无意交互而产生严重噪声。对此类反馈进行去噪的常见方法是基于训练损失模式的观察手动设计规则，但该方法劳动密集且所得规则常缺乏跨场景泛化能力。为克服这些局限，我们提出RuleAgent——一种基于语言智能体的框架，通过模拟现实数据专家自主发现推荐去噪规则。相较于高成本的人工规则挖掘流程，RuleAgent能实现快速动态的规则发现，确保对演化数据与多变场景的适应性。为实现这一目标，RuleAgent配备定制化的画像、记忆、规划与行动模块，并利用反思机制增强其规则发现的推理能力。此外，为避免规则发现过程中的频繁重训练，我们提出LossEraser——一种无需牺牲去噪性能即可简化训练过程的逆向学习策略。基准数据集上的实验表明，相较于现有去噪方法，RuleAgent不仅能获得最优推荐性能，还能生成可泛化的去噪规则，助力研究者实现高效数据清洗。