# When & How to Write for Personalized Demand-aware Query Rewriting in Video Search

arXiv: https://arxiv.org/abs/2602.17667 · HTML: https://mlsanity.com/paper/2602.17667/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [KTO: Model Alignment as Prospect Theoretic Optimization](https://mlsanity.com/paper/2402.01306/) (2024)
- [Large Language Model based Long-tail Query Rewriting in Taobao Search](https://mlsanity.com/paper/2311.03758/) (2023)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models](https://mlsanity.com/paper/2312.11036/) (2023)
- [Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search](https://mlsanity.com/paper/2303.11009/) (2023)
- [Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants](https://mlsanity.com/paper/2502.00537/) (2025)
- [GuRE:Generative Query REwriter for Legal Passage Retrieval](https://mlsanity.com/paper/2505.12950/) (2025)
- [Trie-NLG: Trie Context Augmentation to Improve Personalized Query Auto-Completion for Short and Unseen Prefixes](https://mlsanity.com/paper/2307.15455/) (2023)
- [VALUE: Value-Aware Large Language Model for Query Rewriting via Weighted Trie in Sponsored Search](https://mlsanity.com/paper/2504.05321/) (2025)
- [Crafting the Path: Robust Query Rewriting for Information Retrieval](https://mlsanity.com/paper/2407.12529/) (2024)
- [IterQR: An Iterative Framework for LLM-based Query Rewrite in e-Commercial Search System](https://mlsanity.com/paper/2504.05309/) (2025)
- [Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation](https://mlsanity.com/paper/2505.20966/) (2025)
- [CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms](https://mlsanity.com/paper/2510.10095/) (2025)

## Abstract (arXiv)

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%.