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

LiRank: Industrial Large Scale Ranking Models at LinkedIn

We propose a novel approach for instance-level image retrieval. It produces a
global and compact fixed-length representation for each image by aggregating
many region-wise descriptors. In contrast to previous works employing
pre-trained deep networks as a black box to produce features, our method
leverages a deep architecture trained for the specific task of image retrieval.
Our contribution is twofold: (i) we leverage a ranking framework to learn
convolution and projection weights that are used to build the region features;
and (ii) we employ a region proposal network to learn which regions should be
pooled to form the final global descriptor. We show that using clean training
data is key to the success of our approach. To that aim, we use a large scale
but noisy landmark dataset and develop an automatic cleaning approach. The
proposed architecture produces a global image representation in a single
forward pass. Our approach significantly outperforms previous approaches based
on global descriptors on standard datasets. It even surpasses most prior works
based on costly local descriptor indexing and spatial verification. Additional
material is available at www.xrce.xerox.com/Deep-Image-Retrieval.

本文提出了一种新方法进行实例级别的图像检索，通过聚合多个基于区域的描述符为每个图像生成全局紧凑的固定长度表示，该方法基于一个特定图像检索任务训练的深度架构，利用排序框架来学习卷积和投影权重，并采用区域建议网络来学习应汇集哪些区域以形成最终的全局描述符。