Facebook’s objective is to ensure users encounter posts that resonate with their interests and values. This includes posts from individuals and Facebook Groups they are connected with, as well as posts from new sources that are deemed relevant.
There are two types of content that the Facebook algorithm brings to users’ feeds:Connected content: This includes posts from friends, aFacebook Groups and liked pages.
- Recommended content: These are posts that are likely to interest the user, coming from pages or people they do not follow.
NOTE: There is also a third type of content, namely Facebook ads, but they are governed by targeted marketing strategies rather than the platform’s core algorithm.
As of 2025, the Facebook algorithm has transformed into a sophisticated, AI-driven system focused on delivering content that resonates with users’ preferences.
While Meta acknowledges the algorithm’s imperfections and the possibility that it may never achieve flawlessness, there’s a clear commitment to evolving and refining its approach to align with user desires.
Amidst its intricate nature and the integration of advanced technologies such as AI and machine learning, the Facebook Newsfeed algorithm can be distilled into four primary ranking factors.
Here are the four ranking factors that Facebook uses for content evaluation:
- Inventory
- Signals
- Predictions
- Relevance
A. Inventory
Initially, the algorithm conducts an inventory of potential feed content. This includes posts shared by friends, Facebook pages followed by the user, and groups they are part of. Importantly, any content that breaches Facebook’s Community Standards is immediately excluded from consideration.
B. Signals
Next, the algorithm evaluates various ‘signals’ or ranking factors to gauge how relevant each piece of content is to the user.
These signals are numerous and diverse, such as the timing of the post, the identity of the poster, the user’s level of interaction with the poster, content type (e.g. links, photos, or videos), user engagement with similar posts, the user’s local time, and even the speed of their internet connection.
C. Predictions
Using these signals, the algorithm makes personalized predictions about the content’s relevance and value to the user. For instance, if a user interacts more with branded posts during early mornings, the algorithm will adjust to show more of those posts during that timeframe.
D. Relevance
Finally, each piece of content is assigned a ‘relevance score.’ Content with higher scores gets priority in the Feed.
After organizing the connected content, the algorithm begins to incorporate recommended content into the user’s Feed. To avoid monotony, it avoids showing consecutive posts from the same creator or a series of similar content types back-to-back.
