RAIE: Region-Aware Incremental Preference Editing with LoRA for LLM-based Recommendation
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 ...
| Authors | Jin Zeng, Yupeng Qi, Hui Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 February 2026 |
| Last updated at source | 21 March 2026 |
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