Generating a Billion Personal News Feeds: With exponential growth of information and improved access, there is more and more data and not enough time to digest it. Facebook’s News Feed attempts to solve this by offering a way to show the most relevant content to each individual person. We create billions of personalized experiences by ranking stories for each person. Over the years, News Feed ranking has evolved to use large-scale machine learning techniques, driving to maximize the value created for each individual. Ranking and organizing the content in a unique way for a billion of users poses unique challenges. Each time a person visits their News Feed, we need to find the best piece of content out of all the available stories for them and put it at the top of Feed, where people are most likely to see it. To accomplish this, we model each person, attempting to figure out which friends, pages, and topics they care most about, and pick the stories and ordering they will find most interesting. In addition to the machine learning problems we work on for directing those choices, another primary area of research is understanding the value we are creating for people. These joint problems of selection and evaluation are essential for delivering continued value in personalized Feeds, and they would not be possible at the huge scale of content and users that Facebook operates at without powerful machine learning and analytics.