People who use platforms like YouTube or TikTok often face an issue where they interact slightly too much with the wrong kind of content - say, fake gummy bear experiments or door hinge tutorials - and then their feed gets flooded with that content.

Ideally recommendation systems would be actually controllable. As in, the user could just say, “Show me content about trees and dogs,” and immediately only receive content about trees and dogs. Instead, they have to repeatedly click “I’m not interested in this” and hope that something eventually happens.

Short of the controllable ideal, it would be nice to have a feature where the user could take a snapshot of their historical interaction data when they were happy with their feed. Then, if their feed got derailed in the future, they could simply revert to an old state.

The user could save multiple snapshots of their data as they pass through different phases in life. At any time, they could switch between feeds based on what they’re interested in. Feeds could be shared with friends. There could even be feed marketplaces. Snapshots should probably be taken automatically, too, in case the user forgets to save and label feeds, which they will.

Of course companies hate deleting personal data. They wouldn’t need to. The recommendation algorithm would simply operate on the correct subset of the user’s interaction history, which might be stored, for instance, as a tree. Most recommendation systems heavily weight recent interactions anyway, so it’s not as if relatively large amounts of data would be discarded.

It seems to me this feature would create a more natural sensation of exploration and exploitation. Right now interating with recommender systems feels like walking through the woods without remembering where you’ve been, so if you end up somewhere bad, you have to stumble around until things are good again. Clearly it would be better if you could just backtrack to a place you know is good.