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I don't think you need voting data. Rather, answer the question: "what subreddit is similar to this particular subreddit". Then you use the degree of overlap in
by lars 15y ago
I don't think you need voting data. Rather, answer the question: "what subreddit is similar to this particular subreddit". Then you use the degree of overlap in subscribers as a distance measure between subreddits. Use a tf*idf like approach, so popular subreddits are weighted less.
Then the similarity of r/programming to r/coding would be based on two numbers:
b = number of people subscribed to both r/coding and r/programming
n = number of people subscribed to r/coding
similarity = b/n
- zyfo 15y agoThis model doesn't take into account orthogonal interest, which is what a recommendation engine would be most useful for. Say I like programming and knitting. Probably the similarity between knitting and programming isn't that big. A model which takes into account voting data means my random encounters with knitting-related news on the front page gets weighted in, which is exactly what one wants. Obviously, how to handle that voting data in a useful manner is a problem, but it's definitely a factor.