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.

翻译：随着互联网服务的快速发展，推荐系统已成为不可或缺的组成部分。其中，混排（重排序）阶段在跨多业务目标分配流量中扮演着关键角色。然而，现有方法常面临分配方案耦合、分数膨胀及可解释性缺失等问题。针对这些挑战，我们提出Uniboost，一个统一的流量分配框架。Uniboost引入后验价值对齐机制，将抽象模型分数校准至具有显式业务语义的锚点指标，显著提升可解释性。此外，该框架采用独立线性提升范式解耦复杂权重方案，实现各方案贡献的精确归因。通过在线A/B测试与深度数据分析，我们验证了Uniboost的有效性，并获得三项关键发现：1）降低加权分数的整体权重可有效缓解非预期的业务干扰，产生更高效的微观流量分配策略；2）事后分析与聚合仪表盘为整体流量分配机制设计提供直观的宏观洞察；3）所提出的"有效完成分数"作为易于获取的后验指标，为内容推荐管线提供可靠锚点。综合实验表明，Uniboost不仅在微观层面提升流量分配效率与推荐性能，更在宏观层面为系统迭代提供指引。因此，本工作为大规模工业推荐系统提供了高效可控的流量调控解决方案。