用生成式语义索引提升电商产品理解，解决长尾商品覆盖不足问题
GSID: Generative Semantic Indexing for E-Commerce Product Understanding
- 基于无结构商品元数据预训练，学习领域内语义嵌入
- 生成适配下游任务的高效语义编码，提升产品理解效果
- 已在真实电商场景落地，适用于长尾商品推荐与搜索
商品信息的结构化表示是电商平台效率的主要瓶颈，尤其在二手电商中更为突出。当前多数商品信息依赖人工设计的类别和属性体系，难以充分覆盖长尾商品，且与买家偏好匹配度不高。为此，我们提出生成式语义索引（GSID），一种数据驱动的商品结构化表示方法。GSID包含两个核心组件：(1) 在无结构商品元数据上进行预训练，学习领域内语义嵌入；(2) 生成更适配下游商品中心任务的高效语义代码。大量实验验证了GSID的有效性，并已在真实电商平台上成功部署，在商品理解及其他下游任务中取得良好表现。
原文摘要 · Abstract (English)
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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