Millions of people visit Quora’s home feed to find engaging high-quality content personalized to their interests. The home feed ranks hundreds of thousands of stories in every request using sophisticated machine learning algorithms that simultaneously optimize for engagement, quality, personalization, and content generation.
Nikhil Garg and Neeraj Agrawal describe the evolution of the home feed’s architecture and share several lessons from building and scaling this system.
Nikhil Garg is an engineering manager at Quora, where he has led the quality, ads, and ML platform teams, among others. He is interested in the intersection of machine learning, distributed systems, and human psychology.
Neeraj Agrawal is an engineering manager on Quora’s infrastructure team, where he leads company-wide efforts to make Quora fast and responsive. He started as an engineer working on server-side performance and has since revamped how the company thinks about performance. In his role as the speed lead, he has built the speed team from the ground up and created a culture of “performance matters” at Quora. Previously, he worked on other parts of the product, including writing the first version of Quora for iPhone and building a distributed, real-time ranking service for the personalized news feed product.
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