![]() “The unique challenge is to perform scoring quickly enough to instantly serve tweets back to the people viewing their timelines, yet have powerful enough models to allow for the best possible quality and future improvements.”Īlong with prediction models, Twitter said similar requirements are applied to its machine learning frameworks. “This scoring step imposes an even greater computational demand on timelines serving infrastructure, as we are now scoring thousands of tweets per second to satisfy all of the timeline requests,” the Twitter engineer noted. It also attempts to gauge a user’s past interactions with authors along with the “strength” and origin of the follower’s relationship to an author. ![]() Twitter’s new ranking model takes into account factors such as the number of re-tweets, likes, the inclusion of images and video and other responses to a post. ![]() The highest-ranked tweets are then display at the top of a user’s timeline, Koumchatzky explained. It then scores those tweets using a relevance model, with the goal of predicting the most relevant comments. The new ranking algorithm gathers all tweets from accounts being followed by an individual user. That alone is a daunting infrastructure task, the company notes. In a blog post, company engineers said their approach leverages an in-house artificial intelligence platform that includes new modeling capabilities.Īmong the results, wrote Nicolas Koumchatzky, a software engineer with Twitter’s AI team called Cortex, are “more relevant timelines now, and in the future, as this opens the door for us to use more of the many novelties that the deep learning community has to offer, especially in the areas of, conversation understanding and media domains.”Ĭurrently, Twitter (NYSE: TWTR) timelines are arranged chronologically based on a user’s last visit. Twitter engineers this week unveiled the social media platform’s ranking algorithm driven by deep neural networks. Machine learning is entering production at Twitter as a way of ranking tweets and boosting engagement.
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