LiRank: Industrial Large Scale Ranking Models at LinkedIn

Fedor Borisyuk, Aman Gupta

Abstract:

We present LiRank, a large-scale ranking framework at LinkedIn
that brings to production state-of-the-art modeling architectures
and optimization methods. We unveil several modeling improve-
ments, including Residual DCN, which adds attention and residual
connections to the famous DCNv2 architecture. We share insights
into combining and tuning SOTA architectures to create a unified
model, including Dense Gating, Transformers and Residual DCN.
We also propose novel techniques for calibration and describe how
we productionalized deep learning based explore/exploit methods.
To enable effective, production-grade serving of large ranking
models, we detail how to train and compress models using quanti-
zation and vocabulary compression. We provide details about the
deployment setup for large-scale use cases of Feed ranking, Jobs
Recommendations, and Ads click-through rate (CTR) prediction.
We summarize our learnings from various A/B tests by eluci-
dating the most effective technical approaches. These ideas have
contributed to relative metrics improvements across the board at
LinkedIn: +0.5% member sessions in the Feed, +1.76% qualified job
applications for Jobs search and recommendations, and +4.3% for
Ads CTR. We hope this work can provide practical insights and
solutions for practitioners interested in leveraging large-scale deep
ranking systems.