Structured representation of product information is a major bottleneck for the efficiency of e-commerce platforms, especially in second-hand ecommerce platforms. Currently, most product information are organized based on manually curated product categories and attributes, which often fail to adequately cover long-tail products and do not align well with buyer preference. To address these problems, we propose \textbf{G}enerative \textbf{S}emantic \textbf{I}n\textbf{D}exings (GSID), a data-driven approach to generate product structured representations. GSID consists of two key components: (1) Pre-training on unstructured product metadata to learn in-domain semantic embeddings, and (2) Generating more effective semantic codes tailored for downstream product-centric applications. Extensive experiments are conducted to validate the effectiveness of GSID, and it has been successfully deployed on the real-world e-commerce platform, achieving promising results on product understanding and other downstream tasks.
@article{arxiv.2509.23860,
title = {GSID: Generative Semantic Indexing for E-Commerce Product Understanding},
author = {Haiyang Yang and Qinye Xie and Qingheng Zhang and Liyu Chen and Huike Zou and Chengbao Lian and Shuguang Han and Fei Huang and Jufeng Chen and Bo Zheng},
journal= {arXiv preprint arXiv:2509.23860},
year = {2025}
}