So, as the sample size approaches infinity, the estimate of the mean of data approaches the true mean, and the deviation becomes tighter, or more accurate with the confidence intervals.
Given the above, is this the reason why predictive algorithms for sites like youtube, or music streaming sites have increasingly destroyed their usefulness for people who are unsure what they want to watch?
There is a limit to what videos I care to watch. I specifically like a particular content creator, not necessarily an entire game. However, youtube has recently been restricting results that I see in suggestions to things either directly related to the last video I watched, or directly related to videos I watch normally. It has become less useful. There used to be a fun game where you click on suggestions until you got to some really random video, and the comments were always filled with ‘I’ve found the dark place on youtube again’ kind of comments, or ‘How the hell did I end up here?’
Nowadays, the suggestions are the same 200 videos, ordered based upon the last video you watched it feels like. Is this due to machine learning creating these very tight confidence intervals of what content I want to view?
I can’t find new things on youtube anymore without specifically wanting to find them, and it is sad. There was an old algorithm that did magical things with suggestions where the level of vagueness in the relation to a previous video was much more entertaining to sift through.
Not sure if this is a good place to ask this question, but I figured it was something that could be discussed from a statistical perspective.