One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation

• URL: http://arxiv.org/abs/2608.20640v1
• Date: Fri, 21 Aug 2026 00:27:32 GMT
• Title: One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation
• Abstract summary: Multi-merchant e-commerce catalogs contain equivalent and related products under different merchant-scoped identifiers.<n>We investigate whether a single hierarchical Semantic ID (sid) representation can support personalized ranking and query reformulation.
• Score: 0.8257831610561874
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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