Yes, to address this I have an FAQ entry for it (https://linklonk.com/about):
"
Is it a filter bubble?
On LinkLonk you pay attention to those who you chose to pay attention to. In a sense, LinkLonk is a filter bubble.
A filter bubble is a problem when a system chooses content to show to you without giving you clear control or an explanation of how it came up with these recommendations.
On LinkLonk the ranking mechanism is transparent and is easy to understand. LinkLonk does not try to guess what you would like. What you see is controlled by your explicit ratings. For example, when you see a recommendation from users, LinkLonk explains what links you have in common with these users.
"
Also, I think "echo chamber" (compared to "filter bubble") is often used to describe a dynamic that emerges in groups - when members of a group reinforce who is in and who is out of the group. I think LinkLonk avoids this problem by not having the concept of a group. Every user decides for themselves who they want to hear from. There is no boundary to reinforce - no echo chamber walls to erect.
I don't know for sure whether it would become a harmful echo chamber or a useful tool that helps you find high signal-to-noise information. I'd like to give it a try to find out.
The theme I get from this post is that technology is becoming more and more frictionless at the expense of our agency.
And when we lose control of what we do next we lose track of where we want to get to.
"The feed" does not fully capture this idea. There are feeds that do not take away your control.
My hobby project is a "feed". But the contents of the feed is fully determined by what items you explicitly upvoted and downvoted.
When you upvote an item, you get stronger connected to other users who upvoted it. Their other upvoted items start ranking higher for you.
When you downvote something, your connection to others who upvoted it becomes weaker. So their future upvote have less weight.
This algorithm is transparent and predictable. This makes it possible to meaningfully interact with it. What you vote on has direct consequences for what you will see in the future. It makes you think about your future self. Where you want to be. When you consider to upvote something you need to answer the question: do you want to get more content from people who found this useful?
If this sounds interesting to you, give it a try at https://linklonk.com/register with invitation code "hn" (a temporary account is created, you don't need to give your email to try). It is very early days, I would appreciate any feedback.
That's a great point! LinkLonk has "collections" for this (similar to "boards" on Pinterest). When you upvote something you put that item into a collection. Every user starts with the "default" collection.
But you can create new ones for each of you distinct areas of interest.
For example, you may want to create a collection called "music" and put music links there. When someone upvotes a link that you also upvoted in "music" then they will only connect to your "music" collection, and not your other collections. LinkLonk tracks the trust at the level of collection-to-collection (not user-to-user).
Personally, I put all general interest stuff into the "default" collection, Machine Learning related links into "ML", movies into "movies".
So far I described how organizing what you liked into collections helps others (ie, they get more focused recommendations from you). Why would you want to do this organization in the first place?
There are a couple of reasons:
- When you go to the history of your ratings (https://linklonk.com/ratings) you can filter it by the collection you put it into. This helps you find the article you liked. It is kind of a bookmarking service this way.
- Normally, the recommendations you see are for all of your collections. But you can filter your recommendations to see only items for a specific collection (e.g., music).
Finally, it is not much effort to keep your likes organized once you get started. LinkLonk knows which collection every recommendation is closest to and when you upvote something it will most likely be added to the right collection automatically. For example, if I liked a blog post about an ML topic and put it into "ML", then the future posts from that blog will go to "ML".
Thanks! I'm planning to make a "Show NH" post soon. Please let me know if you have any suggestions or any issues that I could address before that.
There were ~40 new accounts created from this thread. I didn't really expect that many. That's encouraging.
One thing I noticed is that all of the new users skipped the "Welcome" screen which asks users to enter three links they liked recently. So I will likely remove it before doing the "Shown NH".
I am working on an information system (similar to HN or Reddit) with the focus on maximizing usefulness of information (ie, maximize the ROI of your attention).
You can try it out with a temporary account at https://linklonk.com with invitation code "hn".
Similar to Reddit/HN users submit links and vote on them. The difference is how the votes are used. When you upvote something that was worth your time the system connects you to other users who upvoted it. These are the people who deserve your attention since they have been able to recognize it before you did.
The stronger you are connected to someone - the more weight their future upvotes have for you (ie, their upvoted items show up higher in you list).
Instead of you figuring out what is worth your attention and who to trust, the system takes care of it for you. It keeps track of the signal-to-noise ratio of every user and every RSS feed and then ranks content for you accordingly. All you need to do is:
1. upvote stuff that was worth you time - to connect to good content curators
2. downvote stuff that wasted you time - to disconnect from bad content curators.
This creates a feedback loop that brings you content that is worth your attention. The important part is that it uses your definition of "worth your attention" - whatever you upvoted. You are in control.
Another difference is the pace updates.
Reddit/HN demote items very quickly based on the exponential time-decay component in the ranking score.
On LinkLonk you don't have to keep up with the constantly changing feed. The system shows you the top-20 recommendations and waits for you to mark them as read. Then you get your next top-20 that you have not seen yet. It works at your pace.
It seems like they distinguish between "recommendation making" vs "verifying correctness of content" and Credence is meant to solve the latter:
"Since Credence is not a recommendation system, your thumbs-up and thumbs-down decisions should be based on an objective evaluation of whether a file's description matches its contents, not on matters of taste." https://www.cs.cornell.edu/people/egs/credence/faq.html
- when you upvote an item, everyone else who upvoted the item before you earns some amount of your trust
- the more of your trust someone has earned - the more weight their other upvoted items get for you
- each time they upvote, they put some amount of your trust on that item; so if you stop liking their recommendations the amount of your trust they have will go down over time
- when you downvote an item, you take away your trust from people who upvoted it; they've shown that they are not good curators of content for you, so their upvotes will have less weight for you
In this system you end up paying attention to people who have proven to you to be good curators of content. It optimizes for high signal to noise ratio, where what is signal and what is noise if up to you to decide with your upvotes. We don't have to all agree on what is globally "upvoteworthy".
There is no global reputation system (which can be gamed). Instead, there is a peer-to-peer trust system.
If you are interested in a system like that, then I would like to invite you to my hobby project that works exactly this way. Register with a temporary account (no email required) at https://linklonk.com/register and use code 'hn'.
It is early days and we don't have many users yet. To supplement real users LinkLonk supports RSS feeds as sources of information. Each feed behaves much like a user - the more you upvote content from it, the higher ranked its other entries will be for you. I hope you will find it useful and I'm looking to hear your feedback.
A filter bubble is a problem when a system chooses content to show to you without giving you clear control or an explanation of how it came up with these recommendations.
On LinkLonk the ranking mechanism is transparent and is easy to understand. LinkLonk does not try to guess what you would like. What you see is controlled by your explicit ratings. For example, when you see a recommendation from users, LinkLonk explains what links you have in common with these users. "
Also, I think "echo chamber" (compared to "filter bubble") is often used to describe a dynamic that emerges in groups - when members of a group reinforce who is in and who is out of the group. I think LinkLonk avoids this problem by not having the concept of a group. Every user decides for themselves who they want to hear from. There is no boundary to reinforce - no echo chamber walls to erect.
I don't know for sure whether it would become a harmful echo chamber or a useful tool that helps you find high signal-to-noise information. I'd like to give it a try to find out.