Large language models (LLMs) are increasingly adopted as the backbone of recommender systems. However, user-item interactions in real-world scenarios are non-stationary, making preference drift over time inevitable. Existing model update strategies mainly rely on global fine-tuning or pointwise editing, but they face two fundamental challenges: (i) imbalanced update granularity, where global updates perturb behaviors unrelated to the target while pointwise edits fail to capture broader preference shifts; (ii) unstable incremental updates, where repeated edits interfere with prior adaptations, leading to catastrophic forgetting and inconsistent recommendations. To address these issues, we propose Region-Aware Incremental Editing (RAIE), a plug-in framework that freezes the backbone model and performs region-level updates. RAIE first constructs semantically coherent preference regions via spherical k-means in the representation space. It then assigns incoming sequences to regions via confidence-aware gating and performs three localized edit operations - Update, Expand, and Add - to dynamically revise the affected region. Each region is equipped with a dedicated Low-Rank Adaptation (LoRA) module, which is trained only on the region's updated data. During inference, RAIE routes each user sequence to its corresponding region and activates the region-specific adapter for prediction. Experiments on two benchmark datasets under a time-sliced protocol that segments data into Set-up (S), Finetune (F), and Test (T) show that RAIE significantly outperforms state-of-the-art baselines while effectively mitigating forgetting. These results demonstrate that region-aware editing offers an accurate and scalable mechanism for continual adaptation in dynamic recommendation scenarios. Our code is available at https://github.com/fengaogao/RAIE.

翻译：大型语言模型（LLM）正日益成为推荐系统的核心架构。然而，现实场景中的用户-物品交互具有非平稳性，导致用户偏好随时间推移不可避免地发生漂移。现有的模型更新策略主要依赖于全局微调或逐点编辑，但面临两个根本性挑战：（i）更新粒度不平衡，全局更新会干扰与目标无关的行为，而逐点编辑则无法捕捉更广泛的偏好迁移；（ii）增量更新不稳定，重复编辑会干扰先前的适配，导致灾难性遗忘和推荐结果不一致。为解决这些问题，我们提出了区域感知增量编辑（RAIE），一种冻结主干模型并执行区域级更新的插件式框架。RAIE首先在表示空间中通过球形k均值构建语义连贯的偏好区域，随后通过置信度感知门控将输入序列分配到对应区域，并执行三种局部编辑操作——更新、扩展和新增——以动态修正受影响区域。每个区域配备一个专用的低秩适配（LoRA）模块，该模块仅使用该区域的更新数据进行训练。在推理阶段，RAIE将每个用户序列路由至对应区域，并激活该区域特定的适配器进行预测。在将数据划分为初始化集（S）、微调集（F）和测试集（T）的时间切片协议下，基于两个基准数据集的实验表明，RAIE在显著优于现有先进基线方法的同时，有效缓解了遗忘问题。这些结果证明，区域感知编辑为动态推荐场景中的持续适应提供了一种精确且可扩展的机制。我们的代码公开于 https://github.com/fengaogao/RAIE。