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Comment on Ask HN: Does anyone use Bayesian stats to filter interesting stories on the Net?

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Doesn't the upvote system used by HN/Reddit/etc. accomplish the same thing? Outright spam of the kind detectable by a basic filter would presumably be detected by other users who vote on that content. It also has the added benefit of ranking content based on the kind of common appeal you mention, beyond just filtering outright spam, and avoiding the kind of false positives almost any spam filter would be susceptible to.

I read HN mostly via email newsletter and I'm interested in like 5% of the content, if I read one or two articles from newsletter it's good. The same as with other newsletters I'm subscribe for. Each social network has different content post by different people. If you merge all those places into one thing you can't keep up with the flood of post.

I've once was subscribed to a few subreddits via RSS and I was not able to read even headlines and they were accumulation over time.

I was also trying HN100 RSS (post that reach 100 points on HN), and most of the stuff was not interesting to me. So even reading headlines was a waste of time. Not to mention that I was not able to keep on reading the headlines to have 0 RSS inbox.

What I would like to see only post that I would really like, and I believe that you can train SMAP filter to do that.

And about SMAP filtering I don't want to filter SPAM as in an email SPAM only stuff I'm not interested in and I see a lot of post on first page on HN that I'm not interested in.

I would like stories recommendation based on my interested not common interest on every HN reader. Most HN readers are not average HN reader.

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