Facebook is constantly analyzing your profile information, what you post, what you like, who you interact with, and many other data points to understand your interests and connections. This allows Facebook to come up with relevant suggestions for you in various areas like people you may know, groups to join, events to attend, pages to like, and advertising.
Analyzing Profile Information
When you first create your Facebook profile, you provide basic information like your name, location, education, work, contact info, relationship status, bio, profile/cover photos etc. Facebook’s algorithms analyze all this data to start understanding who you are.
For example, if you list New York City as your location, it can suggest local events, restaurants, news pages and groups to you. If you graduated from Stanford University, it can recommend Stanford alumni groups to join. Your work information is used to suggest professional networking groups and events.
As you add more details to your profile over time, Facebook gains a clearer picture of your demographics, interests, and social connections. This expanded profile data enables better recommendations.
Analyzing Posts and Likes
Everything you post and interact with on Facebook provides signals into what you care about. When you post updates, photos, videos, or articles, Facebook looks at the content itself – text, image, metadata, links etc. – to understand your interests.
For example, if you post hiking photos, it picks up terms like hiking, mountains, nature. If you share articles about cooking, it learns you may like food and recipes. Any hashtags, location tags, captions and keywords are parsed. Image recognition even identifies objects and scenes.
Your likes, reactions and comments on posts by friends, pages, groups also provide useful data. If you frequently like travel photos, Facebook’s algorithms can infer you enjoy travel. Comments and conversations are analyzed using natural language processing to extract topics.
This vein of content analysis from your own sharing and engagement allows Facebook to refine its understanding of your preferences and recommend relevant posts, people, groups and events.
Analyzing Your Network of Connections
Beyond your own profile and activity, Facebook analyzes your social graph – the network of connections and interactions between you and other entities on Facebook.
At the most basic level, if you have a lot of friends who currently live in a certain city or went to a certain university, you are likely to have affinity to that location or school yourself. Neighbors, close friends, family members give especially strong signals.
But Facebook zooms deeper to map the entire structure of your social graph and how different nodes relate to each other. For example, it may notice you interact frequently with a group of friends who share a passion for photography. Even if you have never posted a photo yourself, it can derive you likely enjoy photography based on your trusted connections.
Facebook also looks at friend requests and messages exchanged to understand how well and for how long you’ve known someone. Two-way friendships with years of messaging history provide stronger linkage signals than a one-way follow request between distant acquaintances.
Combining your profile data with graph analysis of your connections and interactions allows Facebook to find similarities and make social recommendations like People You May Know and suggesting your friends to tag in posts.
Tracking Your Web and App Activity
When you are logged into Facebook on the web or mobile apps, it can monitor your browsing and usage activity within these environments. Facebook can see which external sites you visit, which articles you read, which videos you watch, which apps you use, how long you spend on each, and more.
This gives Facebook insight into your interests beyond just what you do directly on Facebook itself. If you spend hours researching new TVs on shopping sites, it will know you may be interested in buying a TV and show related suggestions. If you read news sites about business, you are likely interested in business news.
Facebook may also get activity data from sites and apps that implement its analytics or advertising pixel tools. And for apps that integrate with Facebook Login, it can gain access to additional app usage and purchase activity.
Combining your activity within Facebook’s own apps with external browsing/usage provides a 360-degree view of your digital life to inform recommendation algorithms.
Analyzing Messaging Data
Facebook states that the content of your private messages is not used to target ads or recommendations. However, it does analyze metadata and traffic patterns in messaging without looking at specific content.
For example, Facebook might identify you engage in many 1-to-1 chats with a particular friend. Even if the content is secret, this signals you have a close tie. Facebook can also look at group messaging behavior to analyze your connections without reading the conversations.
The aggregate messaging activity graphs are fed into the social recommendation algorithms. However, Facebook emphasizes it does not use private message content itself for ads or suggests.
Watching How You Engage with Recommendations
Finally, Facebook carefully monitors how you engage with the recommendations it generates – people, posts, pages, groups, events and ads. This feedback loop provides the algorithm with signals to further refine and improve suggestions to make them more useful.
For example, if you consistently dismiss or hide recommendations from a certain source, the algorithm learns that is not relevant and tends to avoid showing similar ones. If you frequently click, like or comment on a certain type of suggested post, it knows you appreciate such content and looks for more of it.
By perpetual monitoring of user response to recommendations, Facebook can iterate and enhance the selection over time, creating an adaptive system tailored to you.
Putting It All Together
Facebook combines signals from these multiple sources – profile info, posts, network graph, external activity, messaging patterns and recommendation feedback – into a data science framework to generate and optimize suggestions.
So that hiking photo, Stanford education, New York City job, witty meme you liked, sharp comment you wrote, and coastal town you visited last summer all feed into Facebook’s multilayered algorithms as data points to inference your interests.
The technology correlates these signals on affinity graphs to make connections like “users who like hiking photos and visited coastal towns frequently engage with surfing communities.” Even if you never posted the terms “surfing”, Facebook can derive that suggestion based on millions of other people’s correlated data.
While the specific math involved is highly complex, in general Facebook uses statistical methods to find patterns and clusters among billions of data points. AI like deep learning helps uncover non-obvious insights like detecting niche interests through image analysis.
Combining correlation engines with AI provides powerful recommendation systems that aim to connect people to content and communities they will likely appreciate but not have discovered on their own. Of course, concerns around data privacy, transparency and responsible use remain vital.
Conclusion
In summary, Facebook’s suggestion algorithms synthesize a wide range of profile attributes, activity analysis, network graphs, external behaviors, messaging metadata and feedback signals to find relevant recommendations personalized to each user. While certainly creepy in some ways, the sophisticated tech also helps people discover new friends, knowledge, and communities they value. Understanding how recommendations work empowers users to engage mindfully.