Quasar: A High-Performance Scoring and Ranking Library

Andris Birkmanis and Fei Chen, LinkedIn

Abstract: 

Quasar is a part of LinkedIn's machine learning platform, focusing on two aspects: feature transformation (including scoring) and ranking. It serves many LinkedIn products which include feed, job recommendation, notification recommendation, people-you-may-know, search, etc. It is one of the leading Java-based, in-production, Internet-scale machine learning ranking libraries in industry. In this talk, we will provide an overview of Quasar and highlight the technical challenges and solutions when we built Quasar, as well as present the way of thinking about ranking.

Andris Birkmanis, LinkedIn

Andris Birkmanis is passionate about technology and knowledge, from biology to economics, from 3D graphics to machine learning. His journey started with hacking 8-bit computers, continued with becoming a professional software developer, and led him to building tools and platforms for machine learning.

Fei Chen, LinkedIn

Fei Chen is a Senior Engineering Manager at LinkedIn Data Organization. She leads a machine learning engineering team to standardize job and location-related LinkedIn data. Before that, she led the ranking infrastructure team to build Quasar, the ubiquitous scoring and ranking at LinkedIn. Prior to LinkedIn, she was a researcher at HP Labs, working on machine learning and data management. Her work has been published at top data management conferences, including CIDR, ICDE, SIGMOD, and VLDB.

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BibTeX
@conference {232953,
author = {Andris Birkmanis and Fei Chen},
title = {Quasar: A High-Performance Scoring and Ranking Library},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,
}