[Rotonde]

That's curious, because in my opinion YouTube recommendations haven't been any good since 2009. Instead of getting interesting, strange and niche content, I'm bombarded with videos that have >100k views, feature clickbait titles and thumbnails and are generally incredibly low effort content.

Methods that work better for a population as a whole might not work better for a large subset of that population, and might even cause users to stop using features entirely. The lack of transparency in recommendation algorithms combined with the homogenizing effect of distributing low-quality content this way is something I find somewhat depressing.

[robinduckett]

Maybe those are just the videos that you're statistically more likely to watch through to the end based on your viewing history...

[alphydan]

Precisely. But that might be a local minimum. "Show him boobs and action trailers" is guaranteed to make him stay another 40min.

But perhaps there is a more risky strategy that takes longer to craft and actually delivers hours and hours of content to the user (but needs to fail longer before getting there).

[5olidor]

It seems like reinforcement learning would be useful, i.e. at a high level, forming a policy for recommendations would require balancing exploration (experimenting with more risky recommendations) vs. exploitation (showing you recommendations that it knows will likely lead to clicks) and using the click-throughs, time spent watching the video, etc. as reward signals.

Does anyone know whether RL is used for recommendation in practical settings, and if so what is the current state of the art?

[pcovington]

This is a very natural avenue and an active area of research at Google/Deep Mind. Stay tuned...