How does Facebook decide what to show you?

1. How Facebook builds the pool of possible content to show you

Every time someone opens Facebook the platform first gathers an “inventory” of posts that could appear — this includes updates from friends, Pages the user follows, content from groups, ads and a growing set of recommended items from outside the user’s network such as Reels and Discovery content ^{[2] [5] [6]}. That inventory, not chronological order, is the raw material the system evaluates, and changes to product features (for example a “Local” or “Explore” tab) expand what counts as candidate content ^[7].

2. Signals and predictions: the data the system uses

Facebook’s ranking systems rely on thousands of signals — who posted, the type of content, when it was published, how the user has interacted with similar posts, and how others engaged with the item — to make personalized predictions about whether a user will like, comment, watch or share a post ^{[1] [8] [9]}. Viewing time, whether someone taps “Show more,” and patterns of past engagement feed machine‑learning models that produce per‑post prediction scores ^{[9] [4]}. Different surfaces (Feed, Reels, Groups) weight signals differently; Reels emphasize watch time and sharing, while Groups honor common interests and participation ^{[6] [7]}.

3. Relevance scores and ranking: how the feed is ordered

After scoring candidate items the platform ranks them by an overall relevance or “ranking” score so the highest‑predicted value content appears first; Meta describes this as showing “posts the system predicts will provide more value for you” higher in the Feed ^{[10] [8]}. Facebook also injects variety — mixing types of posts so users don’t see several of the same format in a row — and will demote content that violates community standards or that its models label as clickbait or low‑quality ^{[10] [11]}.

4. User controls, transparency and the feedback loop

Users influence what they see through direct signals like reacting, commenting, saving, or using Show more / Show less and Hide controls — those explicit choices increase or decrease ranking scores and train the system ^{[4] [8]}. Facebook has added features to explain why ads appear and to let users adjust preferences, but the company retains substantial discretion in how signals are weighed, and only Facebook knows the full internal weighting and model details ^{[4] [5]}.

5. Commercial incentives, discovery and the limits of public reporting

The system balances personal relevance with product and commercial goals: more engaging content keeps people on the platform, and ads are personalized based on engagement history and external data shared by businesses, which affects what a user sees alongside organic posts ^{[4] [12]}. Independent reporting and vendor guides emphasize the same core mechanics but are often written for marketers and reflect an incentive to teach creators how to “beat” the system; the exact internal models, weightings and how Meta balances civic‑value tradeoffs are not fully disclosed in public sources cited here ^{[5] [12]}.

6. What to expect in practice and where questions remain

Practically, expect a personalized mix that favors content you engage with, formats you prefer (more photos if you like photos, more Reels if you watch Reels), and posts that spark interactions among connections, while discovery features will surface material from outside your circle ^{[13] [6] [5]}. Reporting and platform documentation converge on the basic pipeline — inventory, signals, predictions, ranking — but do not reveal exact algorithmic weights or the internal tradeoffs Meta makes when “value” to users conflicts with business objectives, leaving a gap between public guidance and full transparency ^{[1] [11]}.