[nihzm]

> This indicates that the persona that this platform created for you is quite homogenous and probably matches closely with many other personas on the platform

To add to this analysis, I think there may also be a feedback component to this problem that exacerbates the issue, since most users are passively using the suggestion algorithm.

In other words, if the suggestion algorithm tends to create a homogenized persona of the user's taste, say, because they don't bother to actively correct it, then this persona is embedded into a cluster of people with similar personas. And because the persona is now closer to said cluster, the suggestions will become even more homogenized. Moreover, since the cluster is mostly composed of passive users, the cluster itself will tend shrink (eg in variance) and to get more homogeneous.

I suspect that most algorithms do not do enough to prevent this global trapping effect, and so even if they have some method to sample "something new" for the user this becomes less and less efficient as more users rely on the algorithm for their suggestions.