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YouTube is Experimenting With Ways To Make Its Algorithm Even More Addictive

While YouTube has publicly said that it’s working on addressing problems that are making its website ever so addictive to users, a new paper from Google, which owns YouTube, seems to tell a different story.

It proposes an update to the platform’s algorithm that is meant to recommend even more targeted content to users in the interest of increasing engagement. Here’s how YouTube’s recommendation system currently works. To populate the recommended-videos sidebar, it first compiles a shortlist of several hundred videos by finding ones that match the topic and other features of the one you are watching. Then it ranks the list according to the user’s preferences, which it learns by feeding all your clicks, likes, and other interactions into a machine-learning algorithm. Among the proposed updates, the researchers specifically target a problem they identify as “implicit bias.” It refers to the way recommendations themselves can affect user behavior, making it hard to decipher whether you clicked on a video because you liked it or because it was highly recommended. The effect is that over time, the system can push users further and further away from the videos they actually want to watch.

To reduce this bias, the researchers suggest a tweak to the algorithm: each time a user clicks on a video, it also factors in the video’s rank in the recommendation sidebar. Videos that are near the top of the sidebar are given less weight when fed into the machine-learning algorithm; videos deep down in the ranking, which require a user to scroll, are given more. When the researchers tested the changes live on YouTube, they found significantly more user engagement. Though the paper doesn’t say whether the new system will be deployed permanently, Guillaume Chaslot, an ex-YouTube engineer who now runs AlgoTransparency.org, said he was “pretty confident” that it would happen relatively quickly.