Quoting myself from a while ago[0]

At reddit many moons ago before machine learning was a buzzword one early iteration of recommendations was based on Jaccard distance using the number of co-voters between subreddits. But with one twist: divide by the size of the smaller subreddit.

relatedness a b =
numerator = | voters on(a) ∩ voters on(b) |
denominator = | voters on(a) ∪ voters on(b) |
weight = min(|voters on(a)|, |voters on(b)|)
numerator / (weight*denominator)

That gives you a directional relatedness, that is programming->python but not necessarily python->programming. Used this way you account for the giant subreddit problem[1] automatically but now the results are less “amitheasshole is related to askreddit” and more like “linguisticshumor is a more niche version of linguistics”.

The great thing is that it’s actually more actionable as far as recommendations go! Everybody has already heard of the bigger version of this subreddit, but they probably haven’t heard of the smaller versions. And it’s self-correcting: as a subreddit gets bigger we are less likely to recommend it, which is great because it needs our help less.

It's also easy to compute this because it lends itself to one giant SQL query that postgres or even sqlite[2] optimises reasonable well. It has some discontinuities around very tiny subredddits, so there was also a hack to just exclude them with a hack heuristic. It does get fairly static so once we've picked 3 subreddits to recommend if you're on subreddit A, if you don't like them we'll just keep showing them anyway. I had a hack in mind for that (use the computed values as random weights so we'll still occasionally show lower-scoring ones) but by this time people much smarter than I took over recommendations with more holistic solutions to the problem we were trying to solve in the first place. Still, as a first pass it worked great and based on my experience I'd recommend simple approaches like this before you break out the the linear algebra.

Side note, I tried co-commenters in addition to co-voters. The results tended more accurate in my spot tests but the difference fell away in more proper cross-validation testing and I didn't look into where the qualitative difference was. But since there are more votes than comments on small subreddits the number of recommendable subreddits was higher with votes. I reasoned that co-submitters (of posts) should be even more accurate but it was thrown off by a small number of spammers and I didn't want to mess with combining those tasks at the time.

[0]: https://news.ycombinator.com/item?id=22178517

[1]: that votes are distributed according to a power law, meaning that everybody has voted on the largest subreddits so most clustering approaches recommend askreddit to everybody. That's okay for product recommendations where "you should buy the most popular CPU, it's most popular for a reason" but for subreddits you already know that so we want a way to bias to the most "surprising" of your votes.

[2]: I prototyped it on sqlite on my laptop and even with close to the production amount of data it ran reasonable well. Not fast, but fine. This was on considerably less traffic to today, mind.