Instagram's New Feature Lets You Tell The App What You Want To See: Here's How

Instagram has expanded its recommendation controls, allowing people to tell the app which topics they want to see more of and which ones they would rather avoid. The move is part of Meta's effort to make Instagram's algorithm feel less like a black box and more like something users can actively shape. The recent update was announced by Instagram chief Adam Mosseri, who said the goal is to give people more control over their experience on the popular social media platform. However, the rollout has also highlighted one request many users still can't make: seeing more posts from the accounts they already follow.What Is Instagram's New Personalisation Feature?With the latest update, users can adjust their recommendations based on interests and topics.

Whether you're into tech content, travel photography, rescue animals or fitness content, Instagram now lets you influence how much of that content appears in your feed.According to Mosseri, the company wants users to have greater influence over the content they consume every day. The new controls focus on interests rather than specific accounts, meaning users can fine-tune recommendations around themes and topics instead of individual creators.There's One CatchWhile users can ask Instagram to show more content related to certain interests, they currently can't tell the algorithm to prioritise posts from people they already follow. As first highlighted by users testing the feature, searching for requests such as "posts from people I follow" reportedly returns an error message stating that no matching results were found. That limitation may disappoint creators and businesses, many of whom have long argued that their content struggles to consistently reach followers despite users choosing to follow them.Why Instagram Says Recommendations Took OverIn a post shared on Threads, Mosseri acknowledged that following accounts used to be one of the main ways people shaped their Instagram experience. Over time, however, recommendations became a much bigger part of the feed. He explained that as users increasingly shifted personal sharing to Stories and direct messages, the traditional feed became less active. Algorithmic recommendations gradually filled that gap by surfacing content from people users had never followed.