[lonk11]

I'm building https://linklonk.com which works this way - you get content ranked based on what you upvoted. This is to make the incentives for voting aligned and help prevent abuse.

I think the problem with karma/reputation systems is that the source of karma are fungible - anyone's upvote has the same effect on the reputation. And this makes it gameable.

A personalized system can solve this by replacing global reputation with user-to-user trust. Now it matters who upvoted - a random bot or a user whose past contributions have been useful to you.

[pbhjpbhj]

>Now it matters who upvoted - a random bot or a user whose past contributions have been useful to you. //

In that system how do you create a ranked list of content for a user to browse? Isn't it going to be very heavy on processing demand?

[lonk11]

Yes, it requires keeping track of how much each user trusts each other user. And then when you rank content for user A, you use the trust table of user A as weights of upvotes.

This is more computationally intensive than sorting by the raw number of upvotes or weight upvotes by karma/popularity.

But I think this is a useful computation - the user can be more confident that the content they is is not astroturfed and comes from trustworthy users.

Details of how trust is calculated: https://linklonk.com/item/3292763817660940288

[z3t4]

You can do the processing in a worker. Maybe even offload it to the client. If there is a live stream a pretrained machine learning model could be used and it could infear who will like what