![]() The recommendation pipeline is made up of three main stages that consume these features: These models aim to answer important questions about the Twitter network, such as, “What is the probability you will interact with another user in the future?” or, “What are the communities on Twitter and what are trending Tweets within them?” Answering these questions accurately enables Twitter to deliver more relevant recommendations. ![]() The foundation of Twitter’s recommendations is a set of core models and features that extract latent information from Tweet, user, and engagement data. While there are many areas of the app where Tweets are recommended-Search, Explore, Ads-this post will focus on the home timeline’s For You feed. Our recommendation system is composed of many interconnected services and jobs, which we will detail in this post. This blog is an introduction to how the algorithm selects Tweets for your timeline. This requires a recommendation algorithm to distill the roughly 500 million Tweets posted daily down to a handful of top Tweets that ultimately show up on your device’s For You timeline. ![]() Twitter aims to deliver you the best of what’s happening in the world right now. ![]()
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