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.

LiRank是LinkedIn上的一个大规模排名框架，它利用先进的建模体系结构和优化方法，实现了最新的建模改进并提出了新技术来生产化深度学习的探索/利用方法。通过训练和压缩模型，以及详细介绍Feed排名、职位推荐和广告点击率（CTR）预测的部署设置，实现了大规模使用场景的有效服务。在LinkedIn上，这些想法在各方面都有相对的指标改进。希望这项工作能为有兴趣利用大规模深度排名系统的从业者提供实用的见解和解决方案。

LiRank: 领英的工业级大规模排名模型