RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents

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 that 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, we propose two flexible application paradigms to accommodate different data scales. For full-scale end-to-end denoising, RuleAgent operates directly on the entire dataset and employs an unlearning strategy, LossEraser, to streamline model optimization and avoid frequent retraining. For more resource-efficient scenarios, RuleAgent can extract rules on small-scale data and transfer them to larger systems for scalable deployment. 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.