3 ms·

Using Doc2Vec to Suggest SubReddits

23 points by jmportilla 11y ago

sdrothrock 11y ago
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.
- jmportilla 11y ago
  Yeah, I know its super annoying. Hopefully I'll have time to update the model with lowercase names sometime next week.
deleted 11y ago
[deleted]
Yadi 11y ago
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.
- jmportilla 11y ago
  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.
utunga 11y ago
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 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..
- gojomo 11y ago
  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 http://arxiv.org/abs/1405.4053 [2] http://arxiv.org/abs/1507.07998 http://arxiv.org/abs/1507.07998
- jmportilla 11y ago
  I used the gensim Doc2Vec implementation. You can check out some of the source code here: https://github.com/jmportilla/Reddit2Vec https://github.com/jmportilla/Reddit2Vec
  - utunga 11y ago
    Hi... Thanks for that. Awesome and much appreciated.
riffraff 11y ago
neat, I'd suggest considering spaces as "+" i.e. "cats awww" should be the same as "cats+awww" I guess :)
haxiomic 11y ago
Nice idea :), works well. Spotted a small typo in the examples: pcmasterace+mac should be pcmasterrace+mac (missing an r)
- jmportilla 11y ago
  Thanks! I'll fix it
joelthelion 11y ago
Very cool. Little tip: use "-funny" to get high-quality subs :)

23 points by jmportilla 11y ago

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.

jmportilla 11y ago
Yeah, I know its super annoying. Hopefully I'll have time to update the model with lowercase names sometime next week.

deleted 11y ago

[deleted]

Yadi 11y ago

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.

jmportilla 11y ago
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.

utunga 11y ago

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 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..

gojomo 11y ago
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 http://arxiv.org/abs/1405.4053 [2] http://arxiv.org/abs/1507.07998 http://arxiv.org/abs/1507.07998
jmportilla 11y ago
I used the gensim Doc2Vec implementation. You can check out some of the source code here: https://github.com/jmportilla/Reddit2Vec https://github.com/jmportilla/Reddit2Vec
- utunga 11y ago
  Hi... Thanks for that. Awesome and much appreciated.

riffraff 11y ago

neat, I'd suggest considering spaces as "+" i.e. "cats awww" should be the same as "cats+awww" I guess :)

haxiomic 11y ago

Nice idea :), works well. Spotted a small typo in the examples: pcmasterace+mac should be pcmasterrace+mac (missing an r)

jmportilla 11y ago
Thanks! I'll fix it

joelthelion 11y ago

Very cool. Little tip: use "-funny" to get high-quality subs :)