发表机构
DoorDash Inc.（多 Dash 公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出单一语义产品层级\textit{\texttt{sid}}，分别用于电商发现面排序与搜索页查询改写，经离线及在线实验验证，可提升排序相关性、加购参与度及查询意图保留等性能。
AI 中文摘要
多商家电商目录包含不同商家范围内标识符下的等价及相关产品，导致行为证据在商家间碎片化。同时，专家定义的分类法往往过于粗糙，无法满足细粒度发现需求。本研究探讨单一分层语义ID（\texttt{Semantic ID}，简称\textit{\texttt{sid}}）表示是否可支持个性化排序与查询改写。该层级从产品内容嵌入中学习一次，定义了多个粒度级别的产品概念，各应用可结合自身行为与服务上下文使用。对于排序任务，我们聚合消费者对\textit{\texttt{sid}}前缀的偏好及产品性能，并为候选产品与消费者历史推导序列特征。控制消融实验显示离线相关性有所提升，而完整排序方案的在线评估表明，热门产品的加购参与度更强，冷门产品获得更广泛曝光。对于查询改写任务，我们将查询与会话转换基于\textit{\texttt{sid}}概念，利用该层级进行导航与优化，并根据商家商品范围过滤建议。离线评估显示，其意图保留比分类法更精细，建议质量比原始查询字符串转换更高；在线评估显示，搜索努力减少，且能更早获取可购买产品。这些结果表明，共享语义产品层级可同时支持推荐与搜索，同时保留各应用所需的任务特定上下文。
英文摘要
Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers, fragmenting behavioral evidence across merchants. Expert-defined taxonomies, meanwhile, are often too coarse for fine-grained discovery. We investigate whether a single hierarchical Semantic ID (\sid{}) representation can support personalized ranking and query reformulation. Learned once from product-content embeddings, the hierarchy defines product concepts at multiple granularities that each application combines with its own behavioral and serving context. For ranking, we aggregate consumer affinity and product performance over \sid{} prefixes and derive sequence features for candidate products and consumer histories. Controlled ablations show improved offline relevance, while online evaluation of the full ranking treatment shows stronger top-slot add-to-cart engagement and broader exposure for less-popular products. For query reformulation, we ground queries and session transitions in \sid{} concepts, use the hierarchy for navigation and refinement, and filter suggestions against the merchant's assortment. Offline evaluation shows finer intent preservation than taxonomy and higher-quality suggestions than raw query-string transitions; online evaluation shows reduced search effort and earlier access to purchasable products. These results show that a shared semantic product hierarchy can support both recommendation and search while preserving the task-specific context required by each application.
