@inproceedings{yang-etal-2025-gsid,
title = "{GSID}: Generative Semantic Indexing for {E}-Commerce Product Understanding",
author = "Yang, Haiyang and
Xie, Qinye and
Zhang, Qingheng and
Yu, Chen Li and
Zou, Huike and
Lian, Chengbao and
Han, Shuguang and
Huang, Fei and
Chen, Jufeng and
Zheng, Bo",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.78/",
doi = "10.18653/v1/2025.emnlp-industry.78",
pages = "1113--1121",
ISBN = "979-8-89176-333-3",
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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<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 Generative Semantic InDexings (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.</abstract>
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%0 Conference Proceedings
%T GSID: Generative Semantic Indexing for E-Commerce Product Understanding
%A Yang, Haiyang
%A Xie, Qinye
%A Zhang, Qingheng
%A Yu, Chen Li
%A Zou, Huike
%A Lian, Chengbao
%A Han, Shuguang
%A Huang, Fei
%A Chen, Jufeng
%A Zheng, Bo
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F yang-etal-2025-gsid
%X 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 Generative Semantic InDexings (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.
%R 10.18653/v1/2025.emnlp-industry.78
%U https://aclanthology.org/2025.emnlp-industry.78/
%U https://doi.org/10.18653/v1/2025.emnlp-industry.78
%P 1113-1121
Markdown (Informal)
[GSID: Generative Semantic Indexing for E-Commerce Product Understanding](https://aclanthology.org/2025.emnlp-industry.78/) (Yang et al., EMNLP 2025)
ACL
- Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, and Bo Zheng. 2025. GSID: Generative Semantic Indexing for E-Commerce Product Understanding. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1113–1121, Suzhou (China). Association for Computational Linguistics.