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

arXiv: https://arxiv.org/abs/2509.23860 · HTML: https://mlsanity.com/paper/2509.23860/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Autoregressive Image Generation using Residual Quantization](https://mlsanity.com/paper/2203.01941/) (2022)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Learning to Tokenize for Generative Retrieval](https://mlsanity.com/paper/2304.04171/) (2023)
- [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://mlsanity.com/paper/2110.06696/) (2021)
- [AliCoCo: Alibaba E-commerce Cognitive Concept Net](https://mlsanity.com/paper/2003.13230/) (2020)
- [OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision](https://mlsanity.com/paper/2204.13874/) (2022)
- [Deep Hierarchical Classification for Category Prediction in E-commerce System](https://mlsanity.com/paper/2005.06692/) (2020)
- [Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach](https://mlsanity.com/paper/2305.18350/) (2023)
- [TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2501.03835/) (2025)
- [MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation](https://mlsanity.com/paper/2403.06747/) (2024)

## Built on by (most cited first, 2 of 2)

- [LLP: LLM-Based Product Pricing in E-commerce](https://mlsanity.com/paper/2510.09347/) (2025)
- [Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL](https://mlsanity.com/paper/2605.14434/) (2026)

## Abstract (arXiv)

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.