偏好塑造相关性：面向个性化生成式检索的跨组件分层语义对齐
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
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中文总结 AI 辅助
针对生成式检索的语义鸿沟、未建模用户行为及推理延迟问题，提出CHAP框架，通过分层语义对齐等机制实现高效个性化检索，在多数据集及在线测试中表现优异。
中文摘要 AI 辅助
生成式检索（GR）作为一种极具前景的范式，凭借强大的候选物品表示能力，可将查询直接映射至语义ID（SIDs）。然而，现有仅从物品内容衍生的SIDs存在语义鸿沟，无法将动态查询意图与静态物品表示对齐；此外，当前生成式范式极少对用户行为序列建模，且始终受限于集束搜索自回归解码的高推理延迟瓶颈。为应对上述挑战，我们提出面向个性化生成式检索的跨组件分层语义对齐（CHAP），这是一种从分层视角出发的新型个性化GR框架。首先，我们设计分层语义对齐模块，以对齐查询的潜在空间与物品的量化路径，同步多粒度语义；其次，我们构建个性化GR框架，通过协同离散SIDs提供结构引导与连续表示实现细粒度语义优化，对用户行为进行建模。值得注意的是，我们引入残差级联生成机制，将成本高昂的多步Transformer解码器限制为单次推理，在提升推理吞吐量的同时减轻信息损失。在三个公开数据集、一个专有工业数据集及在线A/B测试上开展的大量实验，验证了CHAP的优越性，证明了我们方法的有效性与实用价值。代码已公开于该https URL。
英文摘要
Generative Retrieval (GR) has emerged as a promising paradigm by mapping queries directly to Semantic IDs (SIDs) with powerful representation capabilities for candidate items. However, existing SIDs derived solely from item content create a semantic gap, failing to align dynamic query intents with static item representations. Furthermore, current generative paradigms rarely model user behavior sequences and are always bottlenecked by the high inference latency of beam-search autoregressive decoding. To address these challenges, we propose $\textbf{C}$ross-component $\textbf{H}$ierarchical semantic $\textbf{A}$lignment for $\textbf{P}$ersonalized generative retrieval ($\textbf{CHAP}$), a novel personalized GR framework from a hierarchical perspective. First, we design a Hierarchical Semantic Alignment module to align query's latent space with item's quantization path and synchronize multi-granular semantics. Second, we construct a personalized GR framework that models user behavior by synergizing discrete SIDs for structural guidance and continuous representations for fine-grained semantic refinement. Notably, we introduce a Residual Cascading Generation mechanism to restrict the costly multi-step Transformer Decoder to a single-pass inference, boosting inference throughput while mitigating information loss. Extensive experiments on three public datasets, one proprietary industrial dataset, and online A/B tests demonstrate CHAP's superiority, validating the effectiveness and practical value of our approach. The code is publicly available at https://github.com/zzzgm/CHAP.
发表机构
- University of Science and Technology of China（中国科学技术大学）
- Meituan（美团）
机构由 AI 辅助整理，请以论文原文为准。
