LiRank: Industrial Large Scale Ranking Models at LinkedIn
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.
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Fedor Borisyuk
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Mingzhou Zhou
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Qingquan Song
admin_assigned
2024-02-13T12:23:02.542Z
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Qingquan Song
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QQSong
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Siyu Zhu
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Birjodh Tiwana
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Ganesh Parameswaran
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Siddharth Dangi
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Lars Hertel
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Qiang Xiao
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Xiaochen Hou
admin_assigned
2024-02-13T12:24:08.469Z
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Xiaochen Hou
user
HansHou
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Yunbo Ouyang
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Aman Gupta
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Sheallika Singh
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Dan Liu
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Hailing Cheng
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Lei Le
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Jonathan Hung
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Sathiya Keerthi
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Ruoyan Wang
65cae8f88f96de92d05e95b3
Fengyu Zhang
65cae8f88f96de92d05e95b4
Mohit Kothari
65cae8f88f96de92d05e95b5
Chen Zhu
65cae8f88f96de92d05e95b6
Daqi Sun
65cae8f88f96de92d05e95b7
Yun Dai
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Xun Luan
65cae8f88f96de92d05e95b9
Sirou Zhu
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Zhiwei Wang
65cae8f88f96de92d05e95bb
Neil Daftary
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Qianqi Shen
65cae8f88f96de92d05e95bd
Chengming Jiang
65cae8f88f96de92d05e95be
Haichao Wei
claimed_verified
2025-01-24T20:53:28.597Z
6199b1d090692f0d92388473
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Haichao Wei
user
hawei
65cae8f88f96de92d05e95bf
Maneesh Varshney
65cae8f88f96de92d05e95c0
Amol Ghoting
65cae8f88f96de92d05e95c1
Souvik Ghosh
LiRank: Industrial Large Scale Ranking Models at LinkedIn
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, 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.
Residual DCN attention residual connections DCNv2 Dense Gating Transformers calibration explore/exploit methods quantization vocabulary compression