Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations

TL;DR

This paper introduces Lattice, a model space redesign framework for scalable, cost-effective ads recommendation systems that improves quality and efficiency through cross-domain knowledge sharing and system optimizations.

Contribution

The paper presents a novel model space redesign approach that extends multi-domain learning, enabling significant improvements in recommendation quality and cost-efficiency at industry scale.

Findings

10% revenue increase at Meta

11.5% improvement in user satisfaction

20% capacity savings

Abstract

The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale, primarily due to data fragmentation across domains and escalating infrastructure costs that hinder sustained quality improvements. To address this challenge, we propose Lattice, a recommendation framework centered around model space redesign that extends Multi-Domain, Multi-Objective (MDMO) learning beyond models and learning objectives. Lattice addresses these challenges through a comprehensive model space redesign that combines cross-domain knowledge sharing, data consolidation, model unification, distillation, and system optimizations to achieve significant improvements in both quality and cost-efficiency. Our deployment of Lattice at Meta has resulted in 10% revenue-driving top-line metrics gain, 11.5% user satisfaction improvement, 6% boost in conversion rate, with 20% capacity saving.