GSID: Generative Semantic Indexing for E-Commerce Product Understanding

• URL: http://arxiv.org/abs/2509.23860v1
• Date: Sun, 28 Sep 2025 12:58:05 GMT
• Title: GSID: Generative Semantic Indexing for E-Commerce Product Understanding
• Authors: Haiyang Yang, Qinye Xie, Qingheng Zhang, Liyu Chen, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng,
• Abstract summary: We propose textbfGenerative textbfSemantic textbfIntextbfDexings (GSID) to generate product structured representations.<n>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 applications.<n>It has been successfully deployed on the real-world e-commerce platform, achieving promising results on product understanding and other downstream tasks.
• Score: 32.89899469298562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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