arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This disparity creates a significant performance bottleneck for the entire system. To bridge this gap, we propose TARQ, a novel pre-ranking framework. Inspired by generativ
The continuous push for more efficient and effective AI systems in industrial settings, particularly in recommendation engines, drives innovation in areas like pre-ranking to address existing bottlenecks.
Improving pre-ranking efficiency without sacrificing accuracy is critical for scaling large-scale recommendation systems, directly impacting user engagement and revenue for platforms relying on them.
This development suggests a potential improvement in how large-scale recommendation systems can balance computational cost and predictive power, allowing more sophisticated models to be deployed earlier in the ranking pipeline.
- · E-commerce platforms
- · Social media companies
- · Adtech industry
- · AI infrastructure providers
- · Companies with inefficient recommendation systems
- · Legacy pre-ranking model developers
Recommendation systems will become more personalized and efficient, leading to improved user experience and higher conversion rates.
The increased efficiency could enable the deployment of even larger and more complex AI models, further advancing the capabilities of personalized content delivery.
Enhanced recommendation systems might subtly influence user behavior and preferences at scale, impacting cultural trends and consumption patterns.
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Read at arXiv cs.LG