AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow semantics, poor cold-start performance and low serendipity due to reliance on ID-based matching and co-click heuristics. To overcome these challenges, we propose AIGQ (AI-Generated Query architecture), the first end-to-end generative framework for HintQ scenario. AIGQ is built upon three core innovations spanning training paradigm, policy optimization and deployment architecture. First, we propose Interest-Aware List Supervised Fine-Tuning (IL-SFT), a list-level supervised learning approach that constructs train...
| Authors | Jingcao Xu, Jianyun Zou, Renkai Yang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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