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RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
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RuleAgent: Discovering Rules for Recommendation Denoising with Autonomous Language Agents
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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.
Forward citations
Cited by 6 Pith papers
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ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation
ANCHOR creates synthetic noise labels via recommender-in-the-loop LLM agents and trains a parametric recognizer on them to perform supervised denoising of implicit feedback.
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ANCHOR: Agentic Noise Creation Framework for Human Simulation and Denoising Recommendation
A recommender-denoising method that trains a noise recognizer on LLM-simulated user misbehaviors instead of relying on heuristic rules.
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