Welcome to everyday series, where we delve into the fascinating world of technology and innovation. In this edition, we explore a diverse range of topics that showcase the incredible advancements being made in the fields of AI. Discover the inner workings of Twitter's recommendation algorithm, which tailors your feed to deliver engaging and relevant content.
Learn about the groundbreaking use of AI and controvery in chip routing, optimizing the design process in the semiconductor industry.
Be amazed by the discovery of a new proof to the legendary Pythagorean theorem by high schoolers, breathing fresh life into a timeless mathematical concept.
Uncover the details of Facebook's Segment Anything project, revolutionizing image segmentation with AI, improvements in ChatGPT and others. And that's just the beginning!
Join us as we unravel these exciting developments and more, offering you a glimpse into the future of technology and its limitless possibilities.
Twitter recently released its recommendation algorithm on how they recommend tweets. An impressive steps, though a lot is still missing and keeps us wanting more. However today we will try and understand a bit about this before we move to our usual segment.
Twitter's recommendations play a crucial role in providing users with a personalized and engaging experience. By harnessing the power of machine learning models and features, Twitter extracts latent information from tweets, user, and engagement data to answer important questions about the network. This in-depth understanding of the Twitter ecosystem allows for the delivery of more relevant recommendations. In this post, we will explore the three main stages of the recommendation pipeline and the role of Home Mixer in constructing the "For You" timeline.
Overview of Twitter's Recommendation Pipeline
The recommendation pipeline at Twitter consists of three main stages that consume various features and models:
a. Candidate Sourcing: Fetch the best tweets from different recommendation sources.
b. Tweet Ranking: Rank each tweet using a machine learning model.
c. Heuristics and Filters: Apply rules and filters, such as filtering out tweets from users you've blocked, NSFW content, and tweets you've already seen.
These stages work together to deliver personalized recommendations tailored to each user's preferences and interests.
Home Mixer: The Service Behind the "For You" Timeline
Home Mixer, built on Twitter's custom Scala framework, Product Mixer, is responsible for constructing and serving the "For You" timeline. This service acts as the software backbone that connects different candidate sources, scoring functions, heuristics, and filters to create a seamless user experience.