The dominant business model of most social media platforms is selling attention, primarily through advertising. Because platforms are free to users, they need to keep people engaged long enough to show them a meaningful number of ads. This is why engagement metrics — likes, comments, watch time, clicks, and reactions — matter so much: they are an easy-to-measure proxy for attention, which correlates directly with ad opportunities and revenue. Targeted advertising works hand in hand with content personalization, because better predictions about a user's interests help both organic content ranking and ad targeting, raising overall ad performance.
A feed is the central product surface, an ordered stream of content items such as posts, videos, and stories that the platform selects and ranks for each individual user. There are generally two flavors. A Following feed is built mostly from accounts the user has explicitly subscribed to, while a For You or Recommended feed mixes in content the system predicts the user will want, often from accounts they do not follow. To power this mix, platforms maintain two complementary structures: a social graph, which captures who you follow and know, and an interest graph, which captures what you watch, click, and linger on and is used to predict future interests.
Behind every feed is a recommendation system, or recsys, which predicts what content each user is most likely to engage with and serves it in an optimized order. Ranking is the act of sorting candidate content by predicted value, such as the likelihood of watching, enjoying, or interacting, under broader platform objectives like satisfaction, watch time, or meaningful interactions. Because relevance differs by person, the system personalizes the feed rather than showing the same best posts to everyone. Platforms continuously measure the impact of changes through A/B testing, exposing different user groups to different versions of a feature or ranking model to see which performs better on chosen metrics.