MESH：通过异构内容统一扩大检索规模

中文摘要 AI 辅助

优化大规模检索取决于能否在不同内容层级高效筛选候选内容。现代系统常借助碎片化的专业检索模型“动物园”来捕捉新鲜和长尾内容等片段。异构检索系统存在规模偏差挑战，模型容量增益在不同内容层级应用不均。为此提出MESH统一检索扩展框架，通过集成门控偏差校正的模块化架构减轻偏差。通过划分特征空间，减少稀疏项信号与高频参与特征间干扰。经实验验证，该框架提升了稀疏内容的扩展行为，在Pinterest相关引脚平台的在线评估中也取得了多项性能提升，其异步服务策略还提高了系统吞吐量。研究表明MESH是整合碎片化检索基础设施的有前景范式。

英文摘要

Optimizing large-scale retrieval hinges on the ability to efficiently surface candidates across diverse content tiers. However, to capture segments such as fresh and long-tail content, modern systems typically resort to a fragmented "zoo" of specialized retrieval models. This operational complexity is attributed to a fundamental challenge in heterogeneous retrieval systems, the Scaling Bias of Heterogeneity, where model capacity gains do not apply equally across diverse content tiers. To bridge this gap, we propose MESH as a unified retrieval scaling framework that mitigates this bias through a modularized architecture integrated with gated bias correction. By partitioning the feature space into independent domains, MESH enforces a structural inductive bias that reduces interference between sparse-item signals and high-frequency engagement features. This protected gradient path leads to improved scaling behavior for sparse content, empirically validated by a 14 times improvement in the power-law scaling exponent for fresh items. In online evaluations on Pinterest's Related Pins platform, a billion scale item-to-item recommendation system, these improvements translate into a +5.5% lift in fresh-item repins, alongside with 55% improvement in funnel efficiency and +0.46% improvement in user retention. Finally, our asynchronous serving strategy ensures production viability by delivering a 2.87 times improvement in system throughput. Our findings suggest MESH as a promising paradigm for consolidating fragmented retrieval infrastructures into more scalable and ecosystem-aware backbones.

发表机构

• Pinterest Inc.（Pinterest公司）

机构由 AI 辅助整理，请以论文原文为准。