AI 中文总结
研究针对生成式检索中DocID设计问题，提出CRID方法，将DocID解耦并支持增量更新，引入分析框架揭示语义聚类大小作用，实验表明该方法在淘宝语料库上超基线，全流量部署提升了商品交易总额。
AI 中文摘要
生成式检索（GR）将检索制定为序列到序列的生成任务，为每个文档分配一个文档标识符（DocID）并通过自回归解码进行检索，使DocID设计成为检索质量的关键因素。然而，现有基于离散表示学习的方案存在固有冲突问题，且DocID编码目标与系统商业优化目标不匹配。为解决这些限制，我们提出了聚类排序标识符（CRID），它将DocID解耦成语义聚类和商业价值排序，产生无冲突标识符，支持通过聚类内重排进行增量更新。我们还引入了一个分析框架，将检索收益分解为个性化偏好和统计先验泛化，揭示语义聚类大小如何控制这两个组件之间的平衡。在一个3亿项的淘宝电子商务语料库上的实验表明，CRID在top-K命中率方面超过了基于嵌入的最强检索基线，并在全流量部署中带来了1.06%的商品交易总额增长。
英文摘要
Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregressive decoding, making DocID design a critical factor in retrieval quality. However, existing schemes based on discrete representation learning suffer from inherent collision issues and create a mismatch between the DocID's encoding objective and the system's business optimization target. To address these limitations, we propose \textbf{Cluster-Ranked Identifier (CRID)}, which decouples DocID into \textit{semantic clustering} and \textit{business-value ranking}, yielding collision-free identifiers that support incremental updates via intra-cluster reranking. We further introduce an analytical framework that decomposes retrieval gains into \textit{personalized preference} and \textit{statistical prior} generalization, revealing how semantic cluster size governs the balance between the two components. Experiments on a Taobao e-commerce corpus of over 300M items show that CRID surpasses the strongest embedding-based retrieval baseline on top-K Hitrate, and delivers +1.06\% GMV in full-traffic deployment.
CommentsAccepted at EMNLP 2026 Industry Track
