MESH: Scaling Up Retrieval with Heterogeneous Content Unification

Jul 15, 2026

MESH is a unified retrieval scaling framework designed to address the scaling bias of heterogeneity in large-scale recommendation systems. Through a modularized architecture with gated bias correction, MESH partitions the feature space to reduce interference between different content types, leading to a 14x improvement in the power-law scaling exponent for fresh items. In online evaluations on Pinterest's Related Pins platform, MESH demonstrated a +5.5% lift in fresh-item repins, a 55% improvement in funnel efficiency, and a +0.46% improvement in user retention.

Why it matters: MESH represents a significant advance in unifying fragmented retrieval systems, improving scalability and performance for diverse content in large-scale recommendation platforms.

Full story at: arXiv Information Retrieval ↗