Instagram Tests New Ways to Give Users More Control Over Their Feed

Instagram is testing new features that could make it easier for users to personalize what they see in their feed and Reels. 

The updates build on the platform’s “Your Algorithm” feature, giving people quicker ways to tell Instagram which content they want to see more of and which content they’d rather avoid.

Instead of keeping these controls hidden inside the settings, Instagram is experimenting with making them easier to access during everyday browsing. 

In some tests, users can open the personalization menu by pulling down on their feed, while other versions allow similar controls to appear when watching Reels. 

The company is also testing options that let users instantly indicate whether they want to see more content similar to a particular Reel.

The goal is to make content recommendations more accurate by allowing users to provide feedback as they scroll. 

Rather than relying only on likes, comments, and watch time, Instagram wants users to actively shape their own recommendations with simple interactions.

According to Instagram, these features are still in the testing phase, and not every experiment will become a permanent part of the app. 

The company says it wants personalization to become a more central part of the Instagram experience instead of being a hidden setting that few users discover.

While the new controls could help users build a feed that better matches their interests, many users continue to request one feature above all else: seeing more posts from the accounts they already follow. 

This remains one of the most common pieces of feedback shared by the Instagram community.

If these new tools roll out more widely, they could make Instagram’s recommendations feel more transparent and give users greater control over the content that appears every time they open the app.

Overall, Instagram’s latest experiments show that the platform is continuing to invest in personalized content discovery while giving users more influence over how its recommendation system works.