AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation

📰 ArXiv cs.AI

AIGQ is a hybrid generative architecture for e-commerce query recommendation, addressing limitations of traditional methods

advanced Published 23 Mar 2026

Action Steps

Propose a hybrid generative framework to overcome shallow semantics and poor cold-start performance
Design an end-to-end architecture for pre-search query recommendation
Implement AIGQ using AI-generated queries to improve intent capture and demand discovery
Evaluate AIGQ's performance using relevant metrics, such as serendipity and user engagement

Who Needs to Know This

Data scientists and AI engineers on e-commerce teams can benefit from AIGQ to improve query recommendation systems, enhancing user experience and discovery

Key Insight

💡 AIGQ addresses traditional method limitations by using a hybrid generative framework for pre-search query recommendation