Comment by z3t4
3 years ago
I think it can be solved by personalizing the top page(s), so you mostly see the kind of stuff you upvote. If there are a few people up-voting crap you wont see it, but they will see all of it.
3 years ago
I think it can be solved by personalizing the top page(s), so you mostly see the kind of stuff you upvote. If there are a few people up-voting crap you wont see it, but they will see all of it.
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.
>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?
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
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