In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Specifically, WeWrite tackles three key challenges: (1) When to Write: An automated posterior-based mining strategy extracts high-quality samples from user logs, identifying scenarios where personalization is strictly necessary; (2) How to Write: A hybrid training paradigm combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to align the LLM's output style with the retrieval system; (3) Deployment: A parallel "Fake Recall" architecture ensures low latency. Online A/B testing on a large-scale video platform demonstrates that WeWrite improves the Click-Through Video Volume (VV$>$10s) by 1.07% and reduces the Query Reformulation Rate by 2.97%.

翻译：在视频搜索系统中，用户历史行为为识别搜索意图和消除歧义提供了丰富的上下文。然而，利用隐式历史特征的传统方法常面临信号稀释和反馈延迟的问题。为应对这些挑战，我们提出WeWrite——一种新颖的个性化需求感知查询重写框架。具体而言，WeWrite解决了三个关键问题：（1）何时撰写：一种基于后验的自动化挖掘策略从用户日志中提取高质量样本，识别出个性化严格必要的场景；（2）如何撰写：一种结合监督微调（SFT）与组相对策略优化（GRPO）的混合训练范式，使大语言模型（LLM）的输出风格与检索系统对齐；（3）部署：一种并行的“伪召回”架构确保低延迟。在大规模视频平台上的在线A/B测试表明，WeWrite将点击视频播放量（VV＞10秒）提升了1.07%，并将查询重构率降低了2.97%。