Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation

Uniboost is a new traffic allocation framework for recommendation systems that uses posterior value alignment and linear boosting to improve interpretability and efficiency in allocating traffic across business objectives. The system reduces score inflation and decouples allocation plans, demonstrating improved performance in online A/B tests with practical applications for large-scale industrial recommendation systems.

Uniboost addresses a fundamental challenge in modern recommendation systems: fair and efficient traffic allocation across competing business objectives. Recommendation systems serve as critical infrastructure for internet platforms, determining which content reaches users and how resources are distributed. Traditional approaches struggle with score inflation—where models artificially inflate metrics to favor certain objectives—and coupled allocation plans that make it difficult to understand each component's contribution to overall performance.

The framework's innovation centers on two mechanisms: posterior value alignment calibrates abstract model scores to business metrics with explicit meaning, and independent linear boosting decouples weighting schemes. This architectural approach enables granular attribution and transparency, allowing operators to understand precisely how each allocation strategy contributes to system behavior. The research validates effectiveness through online A/B testing, demonstrating that reducing weighted score magnitudes mitigates unintended interference between competing objectives while improving micro-level allocation precision.

For platform operators and developers, Uniboost offers practical benefits beyond theoretical improvements. The introduction of "Effective Completion Score" provides an easily obtainable post-metric that anchors recommendation pipelines reliably. Aggregated dashboards deliver macro-level insights that guide system iteration decisions, creating a feedback loop between operational metrics and engineering priorities. This interpretability advantage reduces the black-box nature of recommendation systems, enabling stakeholders to understand trade-offs between competing business goals.

The research signals growing attention to controllability and transparency in AI systems at scale. As platforms increasingly balance user experience against monetization and operational efficiency, frameworks like Uniboost that provide both performance gains and interpretability become strategically valuable. Future development likely focuses on extending these principles to more complex multi-objective scenarios in industrial settings.

• →Uniboost uses posterior value alignment and linear boosting to improve transparency and reduce score inflation in recommendation systems.
• →The framework successfully decouples complex weighting schemes, enabling precise attribution of each allocation strategy's contribution.
• →Reducing weighted score magnitudes effectively mitigates interference between competing business objectives while improving efficiency.
• →The proposed Effective Completion Score metric provides a reliable anchor for content recommendation pipelines.
• →Online A/B testing validates that the system improves both micro-level traffic allocation precision and macro-level system guidance.