[ako]

It's interesting that these companies go into these different product directions, while i assume they're both looking at similar metrics, and optimizing for the same outcome: engagement and number of users.

Somehow YouTube is seeing more engagement by showing more of the same, and TikTok is seeing even more engagement from showing fresh content.

Maybe the risk of an upward trend in outcomes, that blurs the fact that you could see even better trends by done things differently.

[flyinglizard]

For me, YouTube is literally the same - I get the same videos thrown into my feed in repeat. Apparently this converts well for YT and the result is that my feed is maybe 10% genuine discovery. The rest is shit.

[Clampower]

YouTube also does this weird thing for me where, for example, I watch a couple of videos of an android related YouTube channel about new phones, and then it’ll recommend me videos from 2008 about phones being released then. Lol

[kpennell]

this is Hill climbing, right? https://en.wikipedia.org/wiki/Hill_climbing

[ako]

Yes. How do you deal with hill climbing in product management? How do you know that a 10% improvement in outcomes is bad, and a different approach could have given you 50% or 100%? More experimentation, more 'how might we'? Google is known for trying different approaches (many shades of blue for a link), but somehow none of their experiments indicated fresh content is important? Or is it just a matter of product management 'playing it safe' at Google, where they know 10% outcome improvement is good enough to keep their job?

[MereInterest]

My guess is limited time frames within some standard A/B testing protocol. Give people very similar content over a 1-2 week period, and they'll watch more of it. Give people very similar content over a 6 month period, and they'll get bored and leave. If your testing protocol doesn't look for long term effects, you'll never see the longer effect in any of your tests.