# Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search

arXiv: https://arxiv.org/abs/2404.15675 · HTML: https://mlsanity.com/paper/2404.15675/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search](https://mlsanity.com/paper/2503.21098/) (2025)
- [Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL](https://mlsanity.com/paper/2605.14434/) (2026)
- [From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines](https://mlsanity.com/paper/2604.13468/) (2026)
- [Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan](https://mlsanity.com/paper/2602.06654/) (2026)
- [SSR-GRPO: Integrating Supervision and Semantic IDs into Reinforcement Learning for Dense Retrieval in E-commerce](https://mlsanity.com/paper/2608.19595/) (2026)
- [Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval](https://mlsanity.com/paper/2608.30553/) (2026)

## Abstract (arXiv)

Leveraging generative retrieval (GR) techniques to enhance search systems is an emerging methodology that has shown promising results in recent years. In GR, a text-to-text model maps string queries directly to relevant document identifiers (docIDs), dramatically simplifying the retrieval process. However, when applying most GR models in large-scale E-commerce for personalized item search, we must face two key problems in encoding and decoding. (1) Existing docID generation methods ignore the encoding of efficiency information, which is critical in E-commerce. (2) The positional information is important in decoding docIDs, while prior studies have not adequately discriminated the significance of positional information or well exploited the inherent interrelation among these positions. To overcome these problems, we introduce an efficient Hierarchical encoding-decoding Generative retrieval method (Hi-Gen) for large-scale personalized E-commerce search systems. Specifically, we first design a representation learning model using metric learning to learn discriminative feature representations of items to capture semantic relevance and efficiency information. Then, we propose a category-guided hierarchical clustering scheme that makes full use of the semantic and efficiency information of items to facilitate docID generation. Finally, we design a position-aware loss to discriminate the importance of positions and mine the inherent interrelation between different tokens at the same position. This loss boosts the performance of the language model used in the decoding stage. Besides, we propose two variants of Hi-Gen (Hi-Gen-I2I and Hi-Gen-Cluster) to support online real-time large-scale recall in the online serving process. Hi-Gen gets 3.30% and 4.62% improvements over SOTA for Recall@1 on the public and industry datasets, respectively.