迈向高效且通用的检索：自适应语义量化与残差知识转移
Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
AI总结：
本文提出SA²CRQ框架，通过自适应残差量化和锚定课程残差量化解决语义检索中头部和尾部项的特征模糊与泛化问题，提升冷启动场景下的检索性能。
摘要：
尽管基于语义ID的生成检索在工业应用中实现了高效的端到端建模，但这些方法面临持续的权衡。一方面，数据丰富的头部项常遭遇ID碰撞，导致其独特特征模糊并损害下游任务；另一方面，数据稀疏的尾部项特别是冷启动项在量化过程中易出现语义碎片化，常被映射为孤立离散点，严重阻碍其泛化能力。为解决此问题，我们提出了Anchored Curriculum with Sequential Adaptive Quantization（SA²CRQ）框架。该框架引入了顺序自适应残差量化（SARQ），根据项目路径熵动态分配代码长度，将更长、更具判别性的ID分配给头部项，更短、更具泛化能力的ID分配给尾部项。为缓解数据稀疏性，锚定课程残差量化（ACRQ）组件利用从头部项学习到的冻结语义流形来规范和加速尾部项的表示学习。在大规模工业搜索系统和多个公开数据集上的实验结果表明，SA²CRQ在现有基线上取得了一致的提升，特别是在冷启动检索场景中。
英文摘要：
While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off. On one hand, data-rich head items often suffer from ID collisions, which blur their distinct features and degrade downstream tasks. On the other hand, data-sparse tail items especially cold-start items are prone to semantic fragmentation during quantization; they are often mapped as isolated discrete points, which severely hinders their ability to generalize. To address this issue, we propose the Anchored Curriculum with Sequential Adaptive Quantization ($SA^2CRQ$) framework. The framework introduces Sequential Adaptive Residual Quantization (SARQ) to dynamically allocate code lengths based on item path entropy, assigning longer, discriminative IDs to head items and shorter, generalizable IDs to tail items. To mitigate data sparsity, the Anchored Curriculum Residual Quantization (ACRQ) component utilizes a frozen semantic manifold learned from head items to regularize and accelerate the representation learning of tail items. Experimental results from a large-scale industrial search system and multiple public datasets indicate that $SA^2CRQ$ yields consistent improvements over existing baselines, particularly in cold-start retrieval scenarios.
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。
