Using Doc2Vec to Suggest SubReddits(reddit2vec.com)
reddit2vec.com
Using Doc2Vec to Suggest SubReddits
http://www.reddit2vec.com
7 comments
This is pretty neat, but the biggest problem for me is the case sensitivity; reddit itself doesn't use case sensitivity, so it's hard to remember the exact capitalization of a subreddit name.
Yeah, I know its super annoying. Hopefully I'll have time to update the model with lowercase names sometime next week.
Hi!
Great work. I guess my question is - do you use 'averaging' of word vectors or the Chinese Restaurant process - to get to sub reddit vectors. You describe the Chinese Restaurant process as a "more sophisticated method" that you "can" use, but in my experiments with word2vec and reddit (https://github.com/utunga/gensimred) I quickly discovered that simple averaging just does not work. Averaging has this awful 'revert to mean' thing that turns all the paragraph vectors into a sort of bland gray goo where they are all the same.
If you did use Chinese Restaurant process (I love that phrase - brings back memories of an occasion at a Dim Sum restaurant where this almost literally happened) it'd be great to see any source code you may feel like releasing ;_) ... well, it can't hurt to ask..
Great work. I guess my question is - do you use 'averaging' of word vectors or the Chinese Restaurant process - to get to sub reddit vectors. You describe the Chinese Restaurant process as a "more sophisticated method" that you "can" use, but in my experiments with word2vec and reddit (https://github.com/utunga/gensimred) I quickly discovered that simple averaging just does not work. Averaging has this awful 'revert to mean' thing that turns all the paragraph vectors into a sort of bland gray goo where they are all the same.
If you did use Chinese Restaurant process (I love that phrase - brings back memories of an occasion at a Dim Sum restaurant where this almost literally happened) it'd be great to see any source code you may feel like releasing ;_) ... well, it can't hurt to ask..
The gensim library now offers the 'Paragraph Vector' [1] approach to create vectors for longer ranges of text. It's available in a class named Doc2Vec – but I don't think that's what is being used here.
The Paragraph Vector approach can give interesting results for document-similarity, including similarity after certain 'algebraic'-like additions/subtractions of other topics/word-concepts. [2]
[1] http://arxiv.org/abs/1405.4053
[2] http://arxiv.org/abs/1507.07998
The Paragraph Vector approach can give interesting results for document-similarity, including similarity after certain 'algebraic'-like additions/subtractions of other topics/word-concepts. [2]
[1] http://arxiv.org/abs/1405.4053
[2] http://arxiv.org/abs/1507.07998
I used the gensim Doc2Vec implementation. You can check out some of the source code here: https://github.com/jmportilla/Reddit2Vec
Hi... Thanks for that. Awesome and much appreciated.
Very cool. Little tip: use "-funny" to get high-quality subs :)
Awesome seeing someone use the reddit dataset :)!
Wouldn't a w2v as a recommender for the user might have been better?
Taking user's comments/likes/subreddits as a feature.
Wouldn't a w2v as a recommender for the user might have been better?
Taking user's comments/likes/subreddits as a feature.
I think your thinking of just a classic collaborative filtering recommendation system.A simple w2v system would take into account all words, then have to be filtered by words that are equal to subreddits. Although, I may have misunderstood your suggestion.
neat, I'd suggest considering spaces as "+" i.e. "cats awww" should be the same as "cats+awww" I guess :)
Nice idea :), works well. Spotted a small typo in the examples:
pcmasterace+mac should be pcmasterrace+mac (missing an r)
pcmasterace+mac should be pcmasterrace+mac (missing an r)
Thanks! I'll fix it
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