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Comment by jmduke | Hacker News Reader

I see this argument a lot, and I have two counterpoints:

1. I don't want to have to spend time researching subreddits just to figure out what I'd like. Content aggregators are meant to remove steps from the process, not add them. 2. While the content of smaller subreddits are usually improved, the level of discourse in comments are not. Even if you say 'okay, no meme posts, no meme comments', the fact is that you're going to have a glut of commentors whose first impulse are to crack a joke or make a surface-level comment. The difference in comments between a post on here and a post on /TrueReddit/ are astounding.

(The one pretty ready example to the above are < 100 member subreddits, but those usually have the issue of insufficient content.)

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Expecting one site's landing page to encompass everything for everyone is ridiculous and something a lot of user-generated content sites fail at understanding. You need to filter it to the extent that there's enough for everyone to at least recognize that there's content somewhere there for them, but not overwhelm them.

Between being generically named, being linked in similar subreddit sidebars and showing up in the search, I think it's pretty easy to find what you're looking for or create it yourself and know that it will get more bites than a random website people would have to Google to get to. I would even say that in Reddit's case, there are just so many users from so many different walks of life that it is an easy bet to find a subreddit that suits your fancy in both content and personality.

There's an exploratory phase that anyone who wants to delve into a community has to go through, and if that takes too much time, perhaps that's the user's problem, not the site's.

And how are you going to tell content aggregators what you like? By upvoting/liking? Doesn't it count as spending time?

I don't want to spend time upvoting 50 submissions to tell the recommendation engine what my interests are (and with the current state of technology it will get my interests wrong anyway). I just want to click one button (subscribe to a subreddit) and that's it.

And when I become less interested in a certain topic (fitness, starcraft) I can just press the unsubscribe button once and never see submissions for that topic again.

Ideally -- and take this with a huge caveat, as I'm not exactly in the aggregation business -- measure it by clicks, comments made, and time spent on page/bounce.

Overall, I'd like to see aggregators move to the Pandora model, rather than the Hype Machine one. With Pandora, I can specify a jumping off point (genre, artist, song) but it still refines my tastes from there.

Hype Machine, I've 'liked' hundreds of songs but I still need to manually peruse through specific blogs to find new songs I like. It's unideal.

I believe that in the future that's the way to do it, but not right now.

Besides, I'm not a fan of automatic recommendations as they have a lot of drawbacks. Their major problem is: they won't help you find something you didn't know you liked.

Their major problem is: they won't help you find something you didn't know you liked.

This is patently untrue. It is more likely to find you stuff that you didn't know you like than the lowest common denominator strategy.

Are you talking about any existing recommendation engine that I can try out to prove myself wrong?

Just how exactly will they do it? I'm really interested, maybe I'm missing something?

Simply speaking, if a system knows all I listen to is classical music, it can recommend me a new classical composition, but it cannot all of a sudden recommend me dubstep (which I might actually like).

We're talking about Digg doing this as a differentiation. It is not commonly used because it is very heavy in IO and CPU for individual users (though a site like reddit could take a shortcut just by periodically ramping up some spot high CPU instances and batch generation correlations between users on every subreddit, then staling that until the next run), and sites like Reddit and previously Digg ran on stacks that greatly limited their ability to scale for this.

Regarding music taste, if the system knows that you have a strong correlation with a number of users who like classical but periodically explore new music, what they like will likely be something you might be interested in. This, in practice, is exactly how music discovery happens in the real world (that the discoveries of the people you share musical tastes with are more likely to interest you).

What you are talking about is still incapable of recommending (with a high chance of the recommendation being spot-on) new things. In music a new thing would be a completely different genre, not a new song in your favorite genre.

The system you are describing will find people who have a strong correlation with me and who also like:

1) Classical music and Justin Bieber.

2) Classical music and Metallica.

3) Classical music and Dubstep.

And it will recommend me Bieber, Metallica and Dubstep. I might like Dubstep, but I won't like Bieber and Metallica. How is it better than listening to random songs?

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