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| name | age | message | |
|---|---|---|---|
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README.rdoc | Sun Aug 30 21:07:03 -0700 2009 | readme [Xiang Liang] |
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blending/ | Sun Aug 30 19:25:55 -0700 2009 | blending source [Xiang Liang] |
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include/ | Sun Aug 30 19:23:50 -0700 2009 | source [Xiang Liang] |
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knni-all/ | Mon Aug 31 03:05:01 -0700 2009 | item based knn with language and repos name inf... [Xiang Liang] |
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knni/ | Sun Aug 30 19:23:50 -0700 2009 | source [Xiang Liang] |
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knnu-all/ | Sun Aug 30 21:01:25 -0700 2009 | knnu all [Xiang Liang] |
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knnu/ | Sun Aug 30 19:29:16 -0700 2009 | user based knn [Xiang Liang] |
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knnui/ | Sun Aug 30 19:50:31 -0700 2009 | knnui [Xiang Liang] |
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popular/ | Mon Aug 31 03:38:40 -0700 2009 | recommend popular repos [Xiang Liang] |
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repos/ | Mon Aug 31 05:33:56 -0700 2009 | repos and collaborators [Xiang Liang] |
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results.txt | Sun Aug 30 11:52:39 -0700 2009 | top entry so far based on xlvector's entry [Daniel Haran] |
About this entry
First, thanks to Github for putting on a great contest.
This entry was supposed to be a blend of xlvector and jeremybarnes’ solutions, but due to time it ended up being just xlvector’s.
Liang Xiang (xlvector) will explain his model shortly, Jeremy’s already explained a great deal in his README (read it, it’s great!).
The secret sauce?
More data equals better results.
We also crammed in more heuristics.
Next steps
Anyone wanting to build on this should probably pay close attention to blending. The algorithms themselves aren’t incredibly new, but there’s probably low-hanging fruit with neural networks, decision trees (or combinations) for blending ordered and weighted lists.
Algorithms
Item Based KNN
This algo is in knni directory. I use cosine similarity with inverse user frequency to measure item-item similarity.
User Based KNN
This algo is in knnu directory. I use cosine similarity with inverse item frequency to measure user-user similarity.
Hybrid User/Item KNN
This algo is in knnui directory











