edit: never mind, I just read your GitHub readme. But the question still stands as if "users posting in x, also posted in y" is a good way to infer similarity. Could comparing top-ranked posts be a better comparator?
That said, there are a few subreddits that are too popular and similarity results were too saturated (/r/videos, /r/funny, etc.) so I did a manual override by looking into most commonly mentioned other subreddits, and sometimes into `about` blurb of subreddit).
Please don't consider these recommendation as source of truth! It's just a fun way to discover other subreddits :).
I'm also very open to change this metric to something else - please let me know if you have any recommendations!
[1]: https://github.com/anvaka/sayit#the-data - describes the data, indexing scripts are here: https://github.com/anvaka/sayit/tree/master/scripts
[2]: Manual overrides can be found here https://github.com/anvaka/sayit-data#sayit---recommendation-...
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)
If you guys are interested in seeing how your recommendation work for the entire reddit, I'd be happy to build you a spaceship similar to this one https://github.com/anvaka/word2vec-graph .
I couldn't find an easy way to download the entire recommendation graph, but it would be awesome if we could make it work. My email is the same as this account at gmail, and twitter is all open: https://twitter.com/anvaka
I fooled around a bit with lastfm data for band recommendation and found this sheet quite helpful.
If you are interested in learning more about asymmetrical similarity, here is a great primer by Tversky - http://www.cogsci.ucsd.edu/~coulson/203/tversky-features.pdf
My suggestion would be something much simpler ie. to try to compare content itself e.g. top 1000 posts from each subreddit, and estimate (say) cosine sim + Tfidf. Wouldn't that be a better indicator? Also, instead of pairwise comparison, you could try clustering (HDBSCAN for example) to reduce computational complexity.
But great work, love your visualizations!
I wrote on this topic [1]. My method [2] basically uses simple counts on edge weights, and then estimates the expected edge weight and its variance using Bayesian priors. It then attaches a t-score or p-value to each edge, and then you can filter out edges with too low t-score.
The idea is that weak edges can still be statistically significant if they connect "small" nodes. In any case, the library I wrote includes the implementation of a few other methods, in case they work better for your data type.
[1] https://arxiv.org/abs/1701.07336 [2] http://www.michelecoscia.com/?page_id=287
I think when I created this tool there was no recommendations on reddit.
When it was introduced later on reddit I was contemplating about using reddit's own recommendations, but at that time it was missing a few smaller subreddits, so I just put it of onto the shelf of projects to try.
I'm not sure of a fix for that, but would it be possible/helpful to weigh it by average upvote/downvote of the comments left from users of said sub? Meaning if sub A is about how much baseball sucks, and sub B is about how amazing baseball is, while determining if the 2 are similar you'd find out most posts from sub A to sub B are heavily downvoted and so probably not similar.
I'd still see ability to determine absolute value of relationship as a valuable property of a recommender
Is that really a good metric of similarity? Just myself, I post in several unrelated subreddits semi-regularly from programming to video games to music, art and even stone masonary, i've posted in subreddits for TV shows i've watched, or just completely random things.
I use reddit as a place where I can learn about and interact with people on nearly any subject or topic and I take advantage of that when I can. I'm sure my posting habits aren't that unusual. I'm just not sure that's really an accurate way to gauge similarity.
Jaccard similarity does not count only the number of people who posted to A and B, it checks how many people posted to A, how many people posted to B, and how many of those people posted TOGETHER to A and B. That togetherness gives us hints what is related, and after it is computed, we can divide by the total number of poster to both A and B (independently), which brings the value to something that we can use to compare against other subreddits. If that value is close to 1, it means that almost all users who posted to A have also posted to B. If it is close to 0, then the overlap is much smaller.