EdgeRank Algorithm Algo behind Facebook News Feed (original) (raw)
Last Updated : 23 Jul, 2025
**Every time you open your Facebook account, the first thing you see is your newsfeed. All likes, comments, tags, status updates, shares, and many more such things by your friends. Ever wondered how you get these feeds in a way that you find interesting and not like any random order?
In this article, **we will uncover the mechanics behind Facebook’s newsfeed. We will explore its core components and how they determine what you see. Then, we’ll trace the shift to advanced machine learning algorithms, diving into how they refine your feed with even more precision. Finally, we’ll discuss why understanding this process can help you navigate and optimize your experience on the platform.
Understanding Facebook's Newsfeed Algorithm
Facebook employs a machine learning algorithm that considers certain parameters to find relations between you and the person who has written that post. Prior to employing a machine learning algorithm, the EdgeRank Algorithm was used by Facebook to rank the updates to be displayed on your feed page. This algorithm not only ranks the feed but also sorts it to select which feeds should be shown on your feed at the very beginning and which ones at the very last.
Historical Context
In 2007, a Facebook engineer said in an interview that only about 0.2% of eligible stories make it into a user's newsfeed. That means that your status update is competing with 499 other stories for a single slot in a user's newsfeed.
Evolution to Machine Learning
While the EdgeRank Algorithm laid the foundation for personalized newsfeeds, Facebook has since transitioned to more advanced machine learning algorithms. These modern algorithms consider a broader range of factors, including user behavior, content type preferences, and real-time engagement patterns, to deliver an even more tailored newsfeed experience.
Understanding how the newsfeed algorithm works can help users and content creators optimize their interactions on the platform. By engaging more actively with posts from close connections or creating high-value content like photos and videos, users can influence what appears in their feeds and increase the visibility of their own posts.
Key Components of the EdgeRank Algorithm
There are certain ingredients that are worked upon in the EdgeRank Algorithm before the feeds are served to you. They are Affinity Score, Edge Weight, and Time Decay.
Affinity Score
It means how well the person publishing the post and you are connected. For instance, if you are best friends with that person, and you like, comment, and share each of their posts, then you have a high affinity score with them. So, the algorithm deduces that you probably want to see posts by your friend.
For calculating the affinity score, the following factors are considered:
- **The strength of the action - Each action amongst share, like, tag, comment, etc. has a weight associated with it. So, the more efforts you make with that post, the higher your affinity score. The affinity score is taken into account only if you interact with it. So, just reading through the post without clicking or sharing does not count. So, if your brother is posting about his engagement, marriage, graduation, etc., then his posts hold a somewhat higher affinity score than other posts.
- **How close the person who took the action was to you - Your linkage with the person posting the content is considered an important factor for calculating the affinity score. So, a friend who shares 50 mutual friends will have a higher affinity than a friend who shares 10 mutual friends.
- **How long ago did they take the action? Time is inversely proportional to the affinity score. So, if a person is posting about his birthday and you open your feeds after a week, then definitely those posts are not displayed on your wall.
Importance of Edge Weight and Time Decay
Every post on Facebook is given some weight, i.e., its Importance. In simple terms, a comment on your photo may have more worth than a like or a share. Facebook changes the edge weights to reflect which type of stories they think users will find most engaging. For example, photos and videos have a higher weight than links. So, comments on photos are more likely to be highlighted than comments on links.
As a post gets older, it starts to lose importance. New ones replace them for the slot on your newsfeed. The EdgeRank algorithm not only selects the posts to be displayed on your newsfeed but it also sorts them in order to be displayed on your newsfeed.
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**Conclusion
In summary, Facebook’s newsfeed algorithm has evolved significantly from the original EdgeRank system to advanced machine learning models. Initially, the algorithm focused on factors like affinity, edge weight, and time decay to determine which posts appeared in your feed. Today, machine learning takes these factors further, analyzing user behavior and content engagement in real time for an even more personalized experience. By understanding how the algorithm works, you can better navigate Facebook and optimize your feed interactions, ensuring that you see the content that matters most to you. Whether you’re a user looking to stay connected or a content creator aiming for visibility, engaging thoughtfully with posts and creating engaging content can help improve your experience on the platform.