Distilling accurate user preferences from noisy implicit feedback remains a fundamental bottleneck in recommendation systems, highlighting the need for recommendation denoising. However, real-world data lack explicit noise annotations, forcing existing methods to rely on unsupervised side information or handcrafted heuristics. These approaches often incur high external costs, generalize poorly, or depend on unreliable priors, causing noise misidentification and corrupting true user preference representations. To address these limitations, we propose a paradigm-level reformulation of recommendation denoising. Instead of indirectly inferring noisy interactions through heuristics, our Creation-Recognition paradigm proactively creates labeled noisy interactions and trains a dedicated recognizer to identify them, transforming denoising from heuristic filtering into supervised learning. Based on this paradigm, we present ANCHOR, an agent-based framework inspired by recent LLM-as-User research. ANCHOR simulates user behaviors to generate realistic noise labels and enables supervised denoising through two stages: noise creation and noise recognition. In the noise creation stage, ANCHOR adopts a recommender-in-the-loop agentic architecture to synthesize both diverse out-of-preference noise and informative boundary-adjacent noise. For out-of-preference noise, it implements five extensible simulation mechanisms to approximate major sources of noisy implicit feedback. For boundary-adjacent noise, an adversarial boundary refinement mechanism generates ambiguous interactions that challenge the recognizer and target the decision boundary. In the noise recognition stage, ANCHOR leverages the generated labels to train a reusable parametric recognizer that integrates collaborative signals and semantic representations to detect noise patterns in real interaction data.

翻译：从含噪声的隐式反馈中提取准确用户偏好仍是推荐系统的核心瓶颈，凸显出推荐去噪的必要性。然而，真实数据缺乏显式噪声标注，迫使现有方法依赖无监督辅助信息或人工启发式规则。这类方法常伴随高昂的外部成本、较差的泛化能力或不可靠的先验假设，导致噪声误判并扭曲真实用户偏好表征。为突破这些局限，我们提出推荐去噪的范式级重构。不再通过启发式规则间接推断噪声交互，而是通过"创建-识别"范式主动生成带标签的噪声交互，并训练专用识别器进行检测，从而将去噪从启发式过滤转变为监督学习。基于该范式，我们提出ANCHOR——受近期LLM-as-User研究启发的智能体框架。ANCHOR通过模拟用户行为生成真实噪声标签，分两阶段实现监督去噪：噪声创建与噪声识别。在噪声创建阶段，ANCHOR采用推荐器在环的智能体架构，合成两类噪声：多样化的偏好外噪声与信息性的边界邻接噪声。针对偏好外噪声，设计五种可扩展仿真机制近似主要噪声源；针对边界邻接噪声，通过对抗性边界精炼机制生成挑战识别器并靶向决策边界的模糊交互。在噪声识别阶段，ANCHOR利用生成标签训练可重用参数化识别器，融合协同信号与语义表征检测真实交互数据中的噪声模式。