AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
Jingcao Xu, Jianyun Zou, Renkai Yang, Zili Geng, Qiang Liu, Haihong Tang

TL;DR
This paper introduces AIGQ, an innovative end-to-end generative framework for e-commerce query recommendation that improves intent capture, cold-start performance, and diversity through novel training, policy optimization, and deployment strategies.
Contribution
AIGQ is the first end-to-end generative architecture for HintQ, integrating interest-aware training and hybrid deployment to enhance recommendation quality and user engagement.
Findings
Significant improvements in platform metrics during online A/B tests.
Enhanced cold-start performance and diversity in query recommendations.
Effective modeling of nuanced user intent through session-aware training.
Abstract
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 training samples through session-aware behavior aggregation and interest-guided re-ranking strategy to faithfully model nuanced user intent. Accordingly, we design Interest-aware List Group Relative Policy…
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Taxonomy
TopicsRecommender Systems and Techniques · Information Retrieval and Search Behavior · Caching and Content Delivery
