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：大语言模型（LLMs）正日益成为推荐系统的主干架构。然而，真实场景中的用户-物品交互具有非平稳性，导致偏好漂移随时间推移不可避免。现有模型更新策略主要依赖全局微调或逐点编辑，但面临两个根本性挑战：（i）更新粒度不均衡——全局更新会扰动与目标无关的行为，而逐点编辑则无法捕捉更广泛的偏好变化；（ii）增量更新不稳定——重复编辑会干扰先前的适配，导致灾难性遗忘和不一致的推荐。为解决这些问题，我们提出区域感知增量编辑（RAIE），一种冻结主干模型并执行区域级更新的即插即用框架。RAIE首先通过表示空间中的球面k-means构建语义一致的偏好区域，然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（Update）、扩展（Expand）和添加（Add）——以动态修正受影响的区域。每个区域配备专用的低秩适配（LoRA）模块，该模块仅使用该区域的更新数据进行训练。推理时，RAIE将每个用户序列路由至对应区域，并激活区域特定的适配器进行预测。在将数据划分为设置（S）、微调（F）和测试（T）三阶段的时间切片协议下，基于两个基准数据集的实验表明，RAIE在显著优于最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配提供了一种精确且可扩展的机制。我们的代码开源在 https://github.com/fengaogao/RAIE。