GSID: Generative Semantic Indexing for E-Commerce Product Understanding

2025/09/28 by Haiyang Yang, Yang, Haiyang, Qinghe Zhang +15 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Semantic Web and Ontologies #Web Data Mining and Analysis

paper · pdf · doi:10.48550/arxiv.2509.23860

openalex publication_date 2025/09/28 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

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.

Citations

• TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification
• MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
• C-Pack: Packed Resources For General Chinese Embeddings
• Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
• Learning to Tokenize for Generative Retrieval
• Autoregressive Image Generation using Residual Quantization
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• Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese
• Deep Hierarchical Classification for Category Prediction in E-commerce System
• AliCoCo: Alibaba E-commerce Cognitive Concept Net
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Neural Discrete Representation Learning
• Deep Interest Network for Click-Through Rate Prediction
• Sequence to Sequence Learning with Neural Networks

Cited by

• LLP: LLM-based Product Pricing in E-commerce

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