title: LiRank: Industrial Large Scale Ranking Models at LinkedIn
summary: 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.
paper_authors._id: 65cae8f88f96de92d05e95a0
paper_authors.name: Fedor Borisyuk
paper_authors._id: 65cae8f88f96de92d05e95a1
paper_authors.name: Mingzhou Zhou
paper_authors._id: 65cae8f88f96de92d05e95a2
paper_authors.name: Qingquan Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-13T12:23:02.542Z
paper_authors._id: 6500e83f2233e7d7f566dc1c
paper_authors.avatarUrl: /avatars/42134688fe95cf10c79cdbaafc3abe8f.svg
paper_authors.fullname: Qingquan Song
paper_authors.type: user
paper_authors.user: QQSong
paper_authors._id: 65cae8f88f96de92d05e95a3
paper_authors.name: Siyu Zhu
paper_authors._id: 65cae8f88f96de92d05e95a4
paper_authors.name: Birjodh Tiwana
paper_authors._id: 65cae8f88f96de92d05e95a5
paper_authors.name: Ganesh Parameswaran
paper_authors._id: 65cae8f88f96de92d05e95a6
paper_authors.name: Siddharth Dangi
paper_authors._id: 65cae8f88f96de92d05e95a7
paper_authors.name: Lars Hertel
paper_authors._id: 65cae8f88f96de92d05e95a8
paper_authors.name: Qiang Xiao
paper_authors._id: 65cae8f88f96de92d05e95a9
paper_authors.name: Xiaochen Hou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-13T12:24:08.469Z
paper_authors._id: 644f1b4f0d9ae25ab45e66bb
paper_authors.avatarUrl: /avatars/7c37a434775afae865049ba50707e880.svg
paper_authors.fullname: Xiaochen Hou
paper_authors.type: user
paper_authors.user: HansHou
paper_authors._id: 65cae8f88f96de92d05e95aa
paper_authors.name: Yunbo Ouyang
paper_authors._id: 65cae8f88f96de92d05e95ab
paper_authors.name: Aman Gupta
paper_authors._id: 65cae8f88f96de92d05e95ac
paper_authors.name: Sheallika Singh
paper_authors._id: 65cae8f88f96de92d05e95ad
paper_authors.name: Dan Liu
paper_authors._id: 65cae8f88f96de92d05e95ae
paper_authors.name: Hailing Cheng
paper_authors._id: 65cae8f88f96de92d05e95af
paper_authors.name: Lei Le
paper_authors._id: 65cae8f88f96de92d05e95b0
paper_authors.name: Jonathan Hung
paper_authors._id: 65cae8f88f96de92d05e95b1
paper_authors.name: Sathiya Keerthi
paper_authors._id: 65cae8f88f96de92d05e95b2
paper_authors.name: Ruoyan Wang
paper_authors._id: 65cae8f88f96de92d05e95b3
paper_authors.name: Fengyu Zhang
paper_authors._id: 65cae8f88f96de92d05e95b4
paper_authors.name: Mohit Kothari
paper_authors._id: 65cae8f88f96de92d05e95b5
paper_authors.name: Chen Zhu
paper_authors._id: 65cae8f88f96de92d05e95b6
paper_authors.name: Daqi Sun
paper_authors._id: 65cae8f88f96de92d05e95b7
paper_authors.name: Yun Dai
paper_authors._id: 65cae8f88f96de92d05e95b8
paper_authors.name: Xun Luan
paper_authors._id: 65cae8f88f96de92d05e95b9
paper_authors.name: Sirou Zhu
paper_authors._id: 65cae8f88f96de92d05e95ba
paper_authors.name: Zhiwei Wang
paper_authors._id: 65cae8f88f96de92d05e95bb
paper_authors.name: Neil Daftary
paper_authors._id: 65cae8f88f96de92d05e95bc
paper_authors.name: Qianqi Shen
paper_authors._id: 65cae8f88f96de92d05e95bd
paper_authors.name: Chengming Jiang
paper_authors._id: 65cae8f88f96de92d05e95be
paper_authors.name: Haichao Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-24T20:53:28.597Z
paper_authors._id: 6199b1d090692f0d92388473
paper_authors.avatarUrl: /avatars/72693e4d37ded1542cd2564879fd9a61.svg
paper_authors.fullname: Haichao Wei
paper_authors.type: user
paper_authors.user: hawei
paper_authors._id: 65cae8f88f96de92d05e95bf
paper_authors.name: Maneesh Varshney
paper_authors._id: 65cae8f88f96de92d05e95c0
paper_authors.name: Amol Ghoting
paper_authors._id: 65cae8f88f96de92d05e95c1
paper_authors.name: Souvik Ghosh
paper_title: LiRank: Industrial Large Scale Ranking Models at LinkedIn
paper_summary: 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.
paper_ai_summary: LiRank, a large-scale ranking framework at LinkedIn, integrates advanced modeling techniques and optimization methods to enhance performance in feed ranking, job recommendations, and ads CTR prediction.
paper_ai_keywords: Residual DCN attention residual connections DCNv2 Dense Gating Transformers calibration explore/exploit methods quantization vocabulary compression