Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns. Transformer-based recommenders demonstrate a strong capacity for learning long-range and interpretable dependencies, yet remain vulnerable to behavioral noise that is misaligned with users' true preferences. Recent large language model (LLM)-based approaches attempt to denoise interaction histories through static semantic editing. Such methods neglect the learning dynamics of recommendation models and fail to account for the evolving nature of user interests. To address this limitation, we propose a Dual-view Calibration framework for Sequential Recommendation denoising (DC4SR). Specifically, we introduce a semantic prior, derived from an LLM fine-tuned via labeled historical interactions, to estimate the noise distribution from a semantic perspective. From the learning perspective, we further employ a model-side posterior that infers the noise distribution based on the model's learning dynamics. The disagreement between the two distributions is then leveraged to jointly refine semantic understanding and learning-aware model-side representations. Through iterative updates, dynamic dual-view calibration is achieved for both the global semantic prior and the model-side posterior, enabling consistent alignment with evolving user interests. Extensive experiments demonstrate that DC4SR consistently outperforms strong Transformer-based recommenders and LLM-based denoising methods, exhibiting enhanced robustness across training stages and noise conditions.

翻译：序列推荐旨在通过捕获用户时序意图和物品级转移模式来建模用户兴趣的演变。基于Transformer的推荐器在学习长程可解释依赖关系方面展现出强大能力，但仍易受到与用户真实偏好不一致的行为噪声干扰。近期基于大型语言模型（LLM）的方法尝试通过静态语义编辑对交互历史进行去噪，但忽略了推荐模型的学习动态性，未能考虑用户兴趣的演变特性。为弥补这一局限，我们提出面向序列推荐去噪的双视角校准框架（DC4SR）。具体而言，我们引入由标注历史交互数据微调的LLM推导出的语义先验，从语义视角估计噪声分布；从学习视角出发，进一步利用模型侧后验，基于模型学习动态推断噪声分布。通过利用两种分布之间的分歧，联合优化语义理解与学习感知的模型侧表征。经迭代更新，全局语义先验与模型侧后验可实现动态双视角校准，从而与不断演变的用户兴趣保持一致性。大量实验表明，DC4SR在训练阶段与噪声条件下均展现出增强的鲁棒性，持续超越强基线Transformer推荐器及基于LLM的去噪方法。