According to Facebook, over half of Instagram's roughly 1 billion users visit Instagram Explore to discover videos, photos, livestreams, and Stories each month. Predictably, building the underlying recommendation engine -- which curates the billions of pieces of content uploaded to Instagram -- posed an engineering challenge, not least because it works in real time. In a blog post published this morning, Facebook for the first time peeled back the curtains on Explore's inner workings. Its three-part ranking funnel, which the company says was architected with a custom query language and modeling techniques, extracts 65 billion features and makes 90 million model predictions every second. Before the team behind Explore embarked on building a content recommendation system, they developed tools to conduct large-scale experiments and obtain strong signals on the breadth of users' interests.

Over half of the Instagram community visits Instagram Explore every month to discover new photos, videos, and Stories relevant to their interests. Recommending the most relevant content out of billions of options in real time at scale introduces multiple machine learning (ML) challenges that require novel engineering solutions. We tackled these challenges by creating a series of custom query languages, lightweight modeling techniques, and tools enabling high-velocity experimentation. These systems support the scale of Explore while boosting developer efficiency. Collectively, these solutions represent an AI system based on a highly efficient 3-part ranking funnel that extracts 65 billion features and makes 90 million model predictions every second.