Cascaded Deep Neural Ranking Models in LinkedIn People Search

Abstract

LinkedIn connects the world's professionals to make them more productive and successful. People Search plays an important role in fulfilling this goal by helping members find the most relevant and personalized results through a broad range of queries like names, job titles, skills, companies, locations, etc. It is one of the biggest search verticals at LinkedIn both in terms of engineering footprint and search traffic. In this paper, we present an overview of the People Search system, and discuss how we build and serve deep neural network (DNN) models, leveraging state-of-the-art deep natural language processing (NLP) techniques (e.g., convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT)). We describe our journey of applying deep neural ranking models to a real-life product, including the modeling and system bottleneck challenges, crucial design choices, and lessons learned along the way. We hope a story of our endeavors and successes will provide meaningful insights to other similar systems.