电商检索新方法，联合训练嵌入和码本，解决了传统两阶段训练的错误累积问题。
该研究针对电商场景提出了一种新型生成式检索方法，联合训练嵌入模型和码本。该方法解决了传统两阶段训练中的错误累积问题，并引入同产品簇信息作为额外监督信号。实验表明，该方法显著提升了电商检索性能，同时优化了嵌入和码本学习效果。
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 interactions. As a result, products belonging to the same cluster may be assigned inconsistent IDs by the codebook, further hurting retrieval accuracy. To address these problems, we propose a novel method that jointly trains the embedding model and the codebook, and incorporates same product cluster information as an additional supervision signal. Experimental results demonstrate that our method significantly improves e-commerce retrieval performance while simultaneously enhancing both embedding and codebook learning.