SeqEditor: Editing User Behavior Sequence for Recommendation

User Behavior Sequences (UBSs) are the data foundation for Sequential Recommender Systems (SRSs). Existing studies typically regard these sequences as high-quality behavioral features. However, UBSs in real-world scenarios often suffer from the **quality flaw problem**, manifested as noisy and missing items. This problem prevents SRSs from being fully optimized, hindering model performance. To address this issue, we propose **SeqEditor** to improve the quality of UBSs by editing sequences. Concretely, SeqEditor comprises a _Quality Assessor_ and a _Quality Editor_. The Quality Assessor evaluates UBS quality by estimating local training utility from the alignment between sequence gradients and the prediction-loss gradient on held-out reference users. The LLM-based Quality Editor is optimized by performing supervised fine-tuning (SFT) and reinforcement learning (RL) with interest-aware rewards. This enables it to understand user behavior patterns and identify noise. During the inference stage, SeqEditor effectively improves UBS quality by performing potential missing item supplement and noise removal. SeqEditor can be applied to various SRSs, while its text-based Quality Editor supports cross-domain generalization. Extensive empirical results demonstrate the effectiveness of SeqEditor. Our anonymous code is available at https://anonymous.4open.science/r/SeqEditor_2027.