[2603.00638] RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation

Computer Science > Information Retrieval arXiv:2603.00638 (cs) [Submitted on 28 Feb 2026] Title:RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation Authors:Jin Zeng, Yupeng Qi, Hui Li, Chengming Li, Ziyu Lyu, Lixin Cui, Lu Bai View a PDF of the paper titled RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation, by Jin Zeng and 6 other authors View PDF HTML (experimental) Abstract: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 lo...