CardRewriter：利用知识卡片在短视频平台上进行长尾查询重写
CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms
AI总结：
针对短视频平台长尾查询重写问题，提出基于LLM的CardRewriter框架，通过构建多源知识卡片引导模型生成有效重写，经两阶段训练优化后部署于快手，显著提升了检索与点击指标。
摘要：
短视频平台已迅速成为新一代信息检索系统，用户通过输入查询来获取所需视频。然而，用户查询，特别是长尾查询，常存在拼写错误、表述不完整和意图模糊的问题，导致用户期望与检索结果不匹配。尽管大语言模型（LLMs）在电商领域的长尾查询重写中取得了成功，但它们在短视频平台上表现不佳，因为短视频、直播、微短剧和用户社交网络等专有内容超出了其训练分布。为应对这一挑战，我们引入了CardRewriter，这是一个基于LLM的框架，通过整合特定领域知识来增强长尾查询重写。对于每个查询，该方法聚合与查询相关的多源知识，并将其总结为信息丰富且与查询相关的知识卡片。该卡片随后引导LLM更好地捕捉用户意图，生成更有效的查询重写。我们使用两阶段训练流程优化CardRewriter：先进行监督微调，随后进行组相对策略优化，并配备量身定制的奖励系统以平衡查询相关性和检索有效性。离线实验表明，CardRewriter显著提高了针对专有内容查询的重写质量。在线A/B测试进一步证实，长播率（LVR）和点击率（CTR）获得显著提升，同时主动查询重构率（IQRR）显著降低。自2025年9月起，CardRewriter已部署于中国最大的短视频平台之一快手，每日为数亿用户提供服务。
英文摘要：
Short-video platforms have rapidly become a new generation of information retrieval systems, where users formulate queries to access desired videos. However, user queries, especially long-tail ones, often suffer from spelling errors, incomplete phrasing, and ambiguous intent, resulting in mismatches between user expectations and retrieved results. While large language models (LLMs) have shown success in long-tail query rewriting within e-commerce, they struggle on short-video platforms, where proprietary content such as short videos, live streams, micro dramas, and user social networks falls outside their training distribution. To address this challenge, we introduce \textbf{CardRewriter}, an LLM-based framework that incorporates domain-specific knowledge to enhance long-tail query rewriting. For each query, our method aggregates multi-source knowledge relevant to the query and summarizes it into an informative and query-relevant knowledge card. This card then guides the LLM to better capture user intent and produce more effective query rewrites. We optimize CardRewriter using a two-stage training pipeline: supervised fine-tuning followed by group relative policy optimization, with a tailored reward system balancing query relevance and retrieval effectiveness. Offline experiments show that CardRewriter substantially improves rewriting quality for queries targeting proprietary content. Online A/B testing further confirms significant gains in long-view rate (LVR) and click-through rate (CTR), along with a notable reduction in initiative query reformulation rate (IQRR). Since September 2025, CardRewriter has been deployed on Kuaishou, one of China's largest short-video platforms, serving hundreds of millions of users daily.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
