LiRank：LinkedIn 的工業級大規模排名模型

LiRank: Industrial Large Scale Ranking Models at LinkedIn

February 10, 2024

作者: 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

cs.AI

摘要

我們提出了LiRank，這是LinkedIn的一個大規模排名框架，將最先進的建模架構和優化方法應用於生產。我們揭示了幾項建模改進，包括Residual DCN，它在著名的DCNv2架構中添加了注意力和殘差連接。我們分享了將SOTA架構組合和調整以創建統一模型的見解，包括Dense Gating、Transformers和Residual DCN。我們還提出了用於校準的新技術，並描述了我們如何將基於深度學習的探索/利用方法應用於生產。為了實現有效的生產級大型排名模型服務，我們詳細介紹了如何使用量化和詞彙壓縮來訓練和壓縮模型。我們提供了有關Feed排名、工作推薦和廣告點擊率（CTR）預測等大規模用例的部署設置的詳細信息。我們通過闡明最有效的技術方法，總結了從各種A/B測試中獲得的經驗教訓。這些想法已經在LinkedIn各個方面帶來了相對指標的改善：Feed中會員會話增加了+0.5％，工作搜索和推薦的合格工作申請增加了+1.76％，廣告CTR增加了+4.3％。我們希望這項工作能為有興趣利用大規模深度排名系統的從業者提供實用見解和解決方案。

English

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.