In a recent poll, many LW members expressed interest in a separate website for rational discussion of political topics. The website has been created, but we need a group of volunteers to help us test it and calibrate its recommendation system (see below).
If you would like to help (by participating in one or two discussions and giving us your feedback) please sign up here.
About individual recommendation system
All internet forums face a choice between freedom of speech and quality of debate. In absence of censorship, constructive discussions can be easily disrupted by the inflow of the mind-killed which causes the more intelligent participants to leave or descend to the same level.
Preserving quality thus usually requires at least one of the following methods:
- Appointing censors (a.k.a. moderators).
- Limiting membership.
- Declaring certain topics (e.g., politics) off limits.
On the new website, we are going to experiment with a different method. In brief, the idea is to use an automated recommendation system which sorts content, raising the best comments to the top and (optionally) hiding the worst. The sorting is done based on the individual preferences, allowing each user to avoid what he or she (rather than moderators or anyone else) defines as low quality content. In this way we should be able to enhance quality without imposing limits on free speech.
UPDATE. The discussions are scheduled to start on May 1.
Users’ preferences are determined based on how they rate content, not on how they self-label.
I don’t think users need to know the actual equations (especially since the math is somewhat complicated). But they would easily find out if the numbers are made up (average probabilities for comments they like would be the same as for comments they don’t like).
Our recommendation system is based on principles of collaborative filtering. The average recommendation accuracy depends on the number of ratings in our database. With a relatively small number of users we can distinguish basic population clusters (e.g., left vs right or highbrow vs lowbrow). With a larger dataset we would be able to make more nuanced distinctions.