Comments (3)
@victle, I figure we can switch the discussion for #36 and #47 over here. Am currently re-running everything to make a new up to date enwiki_books.ndjson
, books_embedding_model.h5
(renamed it to make it explicit), and will update the results for rec_books.ipynb. I guess this issue would be the next thing to do on this model, but then am open for suggestions :)
Again, sorry that I was awol for a bit there. Feel free to let me know what you have interest in doing, and of course no stress if you're busy. I'll be fully switching over to another project tomorrow, but would be happy to discuss all this with you still
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This feels super specific to each kind of category, so I really wonder what the best approach is for removing some of these more common terms. I guess one brute force approach is just doing it empirically for different categories? Like, when generating an embedding for movies, comb through some of the top most frequent items and choose for removal? But that does seem like a lot of work
Maybe another easy way is to filter certain wikilinks using regex? It seems to me that an easy one would be removing any links with "wikipedia" or "wikiproject" in them. I wonder how the performance of the model recommendations gets affected by having these extra links in them? It could be harmless for certain categories, but I haven't tested enough to know
And no worries about being away! Everyone's got a life outside GitHub
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I agree that those that have "wikipedia" and especially "wikiproject" should likely be removed. Maybe not the former as there could be things like categories that would be important, but then I'm not sure :) Maybe for major things it'd be best to select them, and for others we could just select those that are present in > X% to be removed.
By the sounds of it this is something that's later on the horizon for both of us though
Feel free to check base here when you have a bit more time, or also let me know if you have something open source you'd want some help on!
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Related Issues (11)
- New recommendation models HOT 6
- Devising ways to best combine recommendations HOT 19
- Allowing users to express disinterest in model.recommend HOT 3
- Implementing simple parsing arguments HOT 4
- Add t-SNE to wikirec HOT 3
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- Create concise requirement and env files
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