Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in industrial e-commerce search remains challenging, given the massive and dynamic product catalogs, strict latency requirements, and the need to align retrieval with downstream ranking goals. In this work, we propose a retrieval framework tailored for real-world recall scenarios, positioning generative retrieval as a recall-stage supplement rather than an end-to-end replacement. Our method, CQ-SID (Category-and-Query constrained Semantic ID), employs category-aware and query-item contrastive learning along with Residu...
| Authors | Jianbo Zhu, Xing Fang, Jing Wang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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