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

arXiv: https://arxiv.org/abs/2605.26424 · HTML: https://mlsanity.com/paper/2605.26424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 11 of 11)

- [Feedback Control of Real-Time Display Advertising](https://mlsanity.com/paper/1603.01055/) (2016)
- [Attention Mixtures for Time-Aware Sequential Recommendation](https://mlsanity.com/paper/2304.08158/) (2023)
- [Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network](https://mlsanity.com/paper/2508.12665/) (2025)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed](https://mlsanity.com/paper/2401.01656/) (2024)
- [Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation](https://mlsanity.com/paper/2204.11589/) (2022)
- [MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation](https://mlsanity.com/paper/2210.07660/) (2022)
- [Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model](https://mlsanity.com/paper/2405.05606/) (2024)
- [Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music](https://mlsanity.com/paper/2603.28994/) (2026)
- [A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search](https://mlsanity.com/paper/2405.15521/) (2024)
- [Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising](https://mlsanity.com/paper/2506.03827/) (2025)

## Abstract (arXiv)

With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays a pivotal role in allocating traffic across diverse business objectives. However, existing approaches often suffer from coupled allocation plans, score inflation, and a lack of interpretability. To address these challenges, we propose Uniboost, a unified traffic allocation framework. Uniboost introduces a posterior value alignment mechanism that calibrates abstract model scores to anchor metrics with explicit business semantics, significantly enhancing interpretability. Furthermore, it employs an independent linear boosting paradigm to decouple complex weighting schemes, enabling precise attribution of each plan's contribution. We validate the effectiveness of Uniboost through online A/B tests and in-depth data analysis, demonstrating three key findings: 1) Reducing the overall weight of weighted scores effectively mitigates unintended business interference, yielding a more efficient micro-level traffic allocation strategy; 2) Post-hoc analyses and aggregated dashboards provide intuitive, macro-level insights that guide the design of the overall traffic allocation mechanism; 3) The proposed "Effective Completion Score" serves as an easily obtainable post-metric that offers a reliable anchor for content recommendation pipelines. Collectively, our experiments show that Uniboost not only improves traffic allocation efficiency and recommendation performance at the micro level but also provides macro-level guidance for system iteration. Thus, this work provides an efficient and controllable traffic regulation solution for large-scale industrial recommendation systems.