Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product embedding model, and then learn a codebook that maps embeddings to product IDs. This cascaded approach suffers from two major issues: (1) error accumulation-if the embedding model in the first stage produces biased representations, the codebook in the second stage cannot correct these errors, degrading final retrieval performance; and (2) codebook learning relies solely on product embeddings and lacks modeling of query-to-product and product-to-product interact...
| Authors | Songtao Fang, Zihao Xu, Shaowei Wei et al. |
|---|---|
| Primary category | cs.IR |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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