LiRank: Industrial Large Scale Ranking Models at LinkedIn
Fedor Borisyuk Mingzhou Zhou Qingquan Song Siyu Zhu Birjodh Tiwana Ganesh Parameswaran Siddharth Dangi Lars Hertel Qiang Xiao Xiaochen Hou Yunbo Ouyang Aman Gupta Sheallika Singh Dan Liu Hailing Cheng Lei Le Jonathan Hung Sathiya Keerthi Ruoyan Wang Fengyu Zhang Mohit Kothari Chen Zhu Daqi Sun Yun Dai Xun Luan Sirou Zhu Zhiwei Wang Neil Daftary Qianqi Shen Chengming Jiang Haichao Wei Maneesh Varshney Amol Ghoting Souvik Ghosh
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 improvements, 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 quantization 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 elucidating 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.