OneBar: An End-to-End Content-Grounded Generative Query Recommendation Framework for E-Commerce Video Feeds

2026-06-13

AI Summary

This work addresses the limitations of conventional query recommendation systems in e-commerce short-video scenarios—namely high latency, misaligned optimization objectives, and the inability of existing generative approaches to handle content noise and shifting user preferences. To overcome these challenges, we propose OneBar, an end-to-end generative query recommendation framework that integrates multimodal video understanding with collaborative user behavior signals. OneBar introduces three key innovations: a collaborative multimodal intent anchoring module, a unified architecture with prompt-aware compression, and a progressive preference internalization strategy that eliminates the need for a separate reward model. Extensive experiments demonstrate that OneBar significantly outperforms the production baseline, achieving relative gains of +16.91% in query impressions, +18.68% in clicks, +20.36% in guided orders, and +21.67% in GMV.

Abstract

Short-video platforms now expose clickable search entries beneath the video player, enabling users to easily express content-induced search intent. However, conventional query recommendation systems on short-video platforms suffer from latency constraints and objective misalignment, while recent generative approaches struggle with noisy content-side metadata and preference drift. To address these issues, we propose OneBar, an end-to-end generative framework for real-time query recommendation for E-Commerce video feeds. OneBar features three key innovations: (1) a collaborative-multimodal intent grounding module that fuses multimodal video understanding and behavior-derived collaborative anchors; (2) a Unified End-to-End architecture equipped with a prompt-compression mechanism for efficient online serving; and (3) a progressive preference learning strategy for efficient preference-internalization, which internalizes hierarchical behavior preferences into the generative policy, eliminating the need for a separately trained reward model. Compared with online base, OneBar increases Query Exposure by 16.91\% and Query Click by 18.68\%, while maintaining a slight Query CTR gain of 0.19\%. The additional search traffic further contributes to 20.36\% more guided orders and 21.67\% higher GMV.

Problem

• query recommendation
• short-video platforms
• generative framework
• preference drift
• noisy metadata

Innovation

• generative query recommendation
• multimodal intent grounding
• end-to-end architecture
• preference internalization
• prompt compression