MESH: Scaling Up Retrieval with Heterogeneous Content Unification
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 spa...
| Authors | Jiaxing Qu, Yilin Chen, Junpeng Hou et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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