GSID: Generative Semantic Indexing for E-Commerce Product Understanding Article Swipe

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Haiyang Yang , Qingguo Xie , Qinghe Zhang , Liyu Chen , Huike Zou , Chuanyu Lian , Shuguang Han , Fei Huang , Jufeng Chen , Bo Zheng ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2509.23860

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2509.23860
PDF: https://arxiv.org/pdf/2509.23860
OA Status: green
OpenAlex ID: https://openalex.org/W4415334801

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DOI: https://doi.org/10.48550/arxiv.2509.23860

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Title: GSID: Generative Semantic Indexing for E-Commerce Product Understanding

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-09-28

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Authors: Haiyang Yang, Qingguo Xie, Qinghe Zhang, Liyu Chen, Huike Zou, Chuanyu Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

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Landing page: https://arxiv.org/abs/2509.23860

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PDF URL: https://arxiv.org/pdf/2509.23860

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