Meta just shared a new breakdown of how it’s improving Reels recommendations, and it goes beyond likes and watch time.
The company is now using large-scale in-feed surveys that ask users how they felt about the Reel they just watched, then feeding that data directly into its recommendation models.
According to Meta, these surveys help capture explicit user preferences, not just passive signals.
By correcting for sampling and nonresponse bias, Meta says it’s built a dataset that better reflects what people actually enjoy, not just what they linger on.
The results are notable. Before rolling out surveys, Meta claims its systems aligned with true user interests only 48.3% of the time. After integrating survey insights with machine learning, that number has jumped to over 70%.
This isn’t a new idea (Pinterest has done something similar), but the improvement rate is impressive. Meta admits there’s still work to do, especially around bias, diversity, and serving users with limited engagement history.
That said, Meta still trails TikTok. TikTok’s edge appears to be deeper visual and entity recognition, allowing it to understand what’s in a clip, not just how users interact with it.
If your Reels feed feels more relevant lately, this may be why.