Getting the News — Evan Williams
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Getting the News — Evan Williams
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Re: Getting the News — Evan Williams
#2I'm not worried about the 'old news' problem he talks about, I'm more worried about the combination of groupthink and algorithms that learn what I read and serve me the same stuff. One of the joys of newspaper reading is that there's a lot of stuff in them that I don't want to read (at first), but over time I get trained to look at topics I wasn't interested in.
When recommenders are applied to news you'll get more of the stuff you like, but you're losing out because you are not seeing the stuff you didn't know that you would like.
Re: Getting the News — Evan Williams
#3> I’ll occasionally type in the domains of other blogs, but if I find myself doing that, it’s a sign I’m not very focused and should get back to work.
This is so true (for me at least). Especially when you reach that point that you actually checked some site only an hour or so earlier and know nothing new of interest with be there.
Re: Getting the News — Evan Williams
#4I'm not worried about the 'old news' problem he talks about, I'm more worried about the combination of groupthink and algorithms that learn what I read and serve me the same stuff. One of the joys of newspaper reading is that there's a lot of stuff in them that I don't want to read (at first), but over time I get trained to look at topics I wasn't interested in. When recommenders are applied to news you'll get more o…
Depends how the recommenders are designed. If they are properly designed, they can remember what they have served you, and automatically inject some diversity into the mix. Actually, they could probably do it much better than current news solutions.
Re: Getting the News — Evan Williams
#5I'm not worried about the 'old news' problem he talks about, I'm more worried about the combination of groupthink and algorithms that learn what I read and serve me the same stuff. One of the joys of newspaper reading is that there's a lot of stuff in them that I don't want to read (at first), but over time I get trained to look at topics I wasn't interested in. When recommenders are applied to news you'll get more o…
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Re: Getting the News — Evan Williams
#6>One thing that I find missing is discovery of non-new content. The web is completely oriented around new-thing-on-top. Our brains are also wired to get a rush from novelty. But most “news” we read really doesn’t matter. And a much smaller percentage of the information I actually care about or would find useful was produced in the last few hours than my reading patterns reflect.
Nassim Taleb wrote about this too in Fooled By Randomness, worth a read:
http://www.curatedalpha.com/2011/nassim-taleb-minimal-exposu...
Re: Getting the News — Evan Williams
#7>iPhone is a biggie, as mentioned above. I don’t touch my iPad much — if so, it’s mostly as an expensive Kindle. I still like the laptop/desktop experience the most. I wish more content was designed for the big screen.
I can't believe the responsive web design trend hasn't done more to take advantage of gigantic screens. Everyone is pushing for smaller screens on mobile devices, while computer screens are way bigger than the 800-1100px width most sites are built for. Fullscreen apps on OSX are getting people used to the gigantic screen, but news in a 600px column doesn't really take advantage.
Pinterest does a good job with this, where they add columns as the browser gets bigger. Some may find it a little more cluttered, but at least it's using the space efficiently.
Re: Getting the News — Evan Williams
#8I'm not worried about the 'old news' problem he talks about, I'm more worried about the combination of groupthink and algorithms that learn what I read and serve me the same stuff. One of the joys of newspaper reading is that there's a lot of stuff in them that I don't want to read (at first), but over time I get trained to look at topics I wasn't interested in. When recommenders are applied to news you'll get more o…
Building a model and personalizing a news stream on a per-user basis is entirely feasible today. Even with very simple ML, you can see some very good real world results.
To avoid the problem of not being able to surface new topics because the user model has converged somewhere, you can use a bandit approach(seriously, who comes up with these names?). By that we mean that you do a bit of exploration by showing news topics drawn at random from some distribution and then adapt your model to what you learned. The naive implementation is "Every random(10,20) news items suggested by the recommender, display 1 drawn at random from the entire corpus. Adapt_Model(user_clicked)"