WeFair：基于人类反馈的保真度感知对齐技术，用于短视频搜索中的个性化查询重写
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search
WeFair 通过保真度感知的人类反馈阶段（演示、偏好和验证）改进个性化查询重写，以减少意图漂移并提升短视频平台的搜索效果。该技术针对短视频搜索对紧凑查询重写器的需求，在严格延迟限制下从模糊查询中恢复用户个性化意图。WeFair-2B 在离线实验中整体准确性和意图保留方面优于强重写基线，在线 A/B 测试显示有效视频播放率相对提升 2.593%，查询重写率相对降低 2.350%。
Published on Oct 30
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Abstract
WeFair improves personalized query rewriting via fidelity-aware human feedback stages—demonstration, preference, and verification—to reduce intent drift and boost search effectiveness in short-video platforms.
Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous queries under strict latency constraints. Existing LLM-based methods have improved rewriting by using synthetic reasoning and subsequent consumption-behavioral feedback. However, overemphasizing subsequent consumption can favor engaging content over the user's immediate need, causing intent drift even when rewritten queries are grounded in behavioral history. We propose WeFair, Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in WeChat short-video search. WeFair uses human feedback according to how directly it reflects personalized intent, assigning feedback fidelity to a progression from demonstration to preference to verification. We first mine actual queries and user reformulations from search sessions, and then screen them for contextual support. In the demonstration stage, a supervised fine-tuning curriculum progresses from large-scale context-to-query learning to reasoning and direct prediction on human reformulation and preservation. In the preference stage, direct preference optimization learns relative preferences from subsequent consumption feedback. In the verification stage, group relative policy optimization rewards exact agreement with reformulation or preservation targets to constrain intent drift. Further analysis shows that broad supervision benefits the full curriculum supervised training, while strict verification achieves better performance than auxiliary rewards. In offline experiments, WeFair-2B outperforms strong rewriting baselines in overall accuracy and intent preservation. An online A/B test shows relative increases of 2.593% in effective video-view rate and a 2.350% relative reduction in query reformulation rate. WeFair-2B has been deployed in the WeChat short-video search system, which serves over 100 million queries per day.
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