DSIRM: Learning Query-Bridged Discrete Semantic Identifiers for E-commerce Relevance Modeling

ArXi:2606.04374v1 Announce Type: cross Despite rapid progress of continuous embeddings for e-commerce search relevance, a long-standing open problem is the difficulty in capturing fine-grained attribute distinctions. While discrete Semantic Identifiers (SIDs) have been widely adopted as a promising alternative, existing SID generation methods rely heavily on unsupervised quantization. In realistic scenarios, the lack of explicit supervision often makes it difficult to dictate which items should share an SID, resulting in limited capability for query-dependent ranking.