由AI驱动：Instagram的探索推荐系统

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This post was originally published on the Facebook AI blog.

这篇文章最初发布在 Facebook AI博客。

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.

超过一半的 Instagram 社区每月访问 Instagram 探索，以发现与他们兴趣相关的新照片、视频和故事。在实时大规模中从数十亿个选项中推荐最相关的内容引入了多个机器学习（ML）挑战，这需要新颖的工程解决方案。

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.

我们通过创建一系列自定义查询语言、轻量级建模技术和工具来应对这些挑战，这些工具使得高速度实验成为可能。这些系统支持Explore的规模，同时提高开发者的效率。总体而言，这些解决方案代表了一个基于高效的三部分排名漏斗的AI系统，该系统每秒提取650亿个特征并进行9000万次模型预测每秒。

In this blog post, we’re sharing the first detailed overview of the key elements that make Explore work, and how we provide personalized content for people on Instagram.

在这篇博客文章中，我们分享了使Explore运作的关键元素的首次详细概述，以及我们如何为Instagram上的人们提供个性化内容。

Developing foundational building blocks of Explore

开发Explore的基础构建块

Before we could execute on building a recommendation engine that tackles the sheer volume of photos and videos uploaded daily on Instagram, we developed foundational tools to address three important needs. We needed the ability to conduct rapid experimentation at scale, we needed to obtain a stronger signal on the breadth of people’s interests, and we needed a computationally efficient way to ensure th...