Owner
lyst
5 indexed repositories · View on GitHub
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lightfm
A Python implementation of LightFM, a hybrid recommendation algorithm.
Python · 5112 stars
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TeX · 45 stars
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Python · 9 stars
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django-urlconf-export
Make URLs for your Django website from anywhere
Python · 5 stars
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Python · 4 stars
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Difficulty 2/5 1-3 hours Newbie friendliness 55/100
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Difficulty 2/5 1-3 hours Newbie friendliness 42/100
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Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Difficulty 4/5 3-5 days Newbie friendliness 20/100
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Difficulty 3/5 1-2 days Newbie friendliness 25/100
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Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 3/5 1-2 days Newbie friendliness 35/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 3/5 1-2 days Newbie friendliness 45/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 5/5 Over a week Newbie friendliness 25/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 3/5 1-2 days Newbie friendliness 38/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Difficulty 5/5 Over a week Newbie friendliness 15/100
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Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 5/5 Over a week Newbie friendliness 25/100
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Difficulty 2/5 1-3 hours Newbie friendliness 38/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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do I need to pass user and item matrixes to predict method for known users and items in fit step Open
Difficulty 3/5 1-2 days Newbie friendliness 25/100
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Difficulty 2/5 1-3 hours Newbie friendliness 52/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 3/5 1-2 days Newbie friendliness 25/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 3/5 1-2 days Newbie friendliness 25/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Difficulty 5/5 Over a week Newbie friendliness 15/100
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Difficulty 5/5 Over a week Newbie friendliness 30/100
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Difficulty 3/5 1-2 days Newbie friendliness 30/100
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Difficulty 5/5 Over a week Newbie friendliness 20/100
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Temporal splitting Open
Difficulty 4/5 3-5 days Newbie friendliness 30/100
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
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Difficulty 5/5 Over a week Newbie friendliness 15/100
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
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Difficulty 4/5 3-5 days Newbie friendliness 28/100
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Difficulty 4/5 3-5 days Newbie friendliness 35/100
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Difficulty 4/5 3-5 days Newbie friendliness 25/100
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Difficulty 5/5 Over a week Newbie friendliness 15/100
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Do I need to provide item/user features for predict() if I've already include the features in fit()? Open
Difficulty 2/5 1-3 hours Newbie friendliness 20/100
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Difficulty 5/5 Over a week Newbie friendliness 25/100
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