# OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion

arXiv: https://arxiv.org/abs/2506.06913 · HTML: https://mlsanity.com/paper/2506.06913/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2025.

## Built on by (most cited first, 14 of 14)

- [OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework](https://mlsanity.com/paper/2603.24422/) (2026)
- [GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models](https://mlsanity.com/paper/2507.06507/) (2025)
- [Revisiting scalable sequential recommendation with Multi-Embedding Approach and Mixture-of-Experts](https://mlsanity.com/paper/2510.25285/) (2025)
- [QP-OneModel: A Unified Generative LLM for Multi-Task Query Understanding in Xiaohongshu Search](https://mlsanity.com/paper/2602.09901/) (2026)
- [Generative Bid Shading in Real-Time Bidding Advertising](https://mlsanity.com/paper/2508.06550/) (2025)
- [LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services](https://mlsanity.com/paper/2603.04946/) (2026)
- [IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters](https://mlsanity.com/paper/2604.18375/) (2026)
- [OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations](https://mlsanity.com/paper/2605.23668/) (2026)
- [QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation](https://mlsanity.com/paper/2606.05671/) (2026)
- [Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment](https://mlsanity.com/paper/2609.19209/) (2026)
- [SIGMA: A Semantic-Grounded Instruction-Driven Generative Multi-Task Recommender at AliExpress](https://mlsanity.com/paper/2602.22913/) (2026)
- [UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval](https://mlsanity.com/paper/2608.03150/) (2026)
- [GenMatch: An End-to-End Generative Matching Framework for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2608.19751/) (2026)
- [WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat](https://mlsanity.com/paper/2610.16979/) (2026)

## Abstract (arXiv)

Query suggestion plays a crucial role in enhancing user experience in e-commerce search systems by providing relevant query recommendations that align with users' initial input. This module helps users navigate towards personalized preference needs and reduces typing effort, thereby improving search experience. Traditional query suggestion modules usually adopt multi-stage cascading architectures, for making a well trade-off between system response time and business conversion. But they often suffer from inefficiencies and suboptimal performance due to inconsistent optimization objectives across stages. To address these, we propose OneSug, the first end-to-end generative framework for e-commerce query suggestion. OneSug incorporates a prefix2query representation enhancement module to enrich prefixes using semantically and interactively related queries to bridge content and business characteristics, an encoder-decoder generative model that unifies the query suggestion process, and a reward-weighted ranking strategy with behavior-level weights to capture fine-grained user preferences. Extensive evaluations on large-scale industry datasets demonstrate OneSug's ability for effective and efficient query suggestion. Furthermore, OneSug has been successfully deployed for the entire traffic on the e-commerce search engine in Kuaishou platform for over 1 month, with statistically significant improvements in user top click position (-9.33%), CTR (+2.01%), Order (+2.04%), and Revenue (+1.69%) over the online multi-stage strategy, showing great potential in e-commercial conversion.