@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://preview.aclanthology.org/ingest-emnlp/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."
}