lyst/lightfm
View on GitHubA Python implementation of LightFM, a hybrid recommendation algorithm.
- Stars
- 5.1k
- Forks
- 724
- Open beginner issues
- 0
- Indexed issues
- 151
- Dominant language
- Python
- License
- Apache-2.0
- Last GitHub push
- Apr 30, 2023
- Latest indexed
- Sep 19, 2026
- Contributing guide
- No contributing guide
- Code of conduct
- No code of conduct
- Beginner labels
- help wanted
- PR merge metrics
- No merged PRs in 30d
-
Difficulty 2/5 1-3 hours Newbie friendliness 55/100
-
Difficulty 2/5 1-3 hours Newbie friendliness 42/100
-
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Difficulty 4/5 3-5 days Newbie friendliness 20/100
-
Difficulty 3/5 1-2 days Newbie friendliness 25/100
-
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 3/5 1-2 days Newbie friendliness 35/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 3/5 1-2 days Newbie friendliness 45/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 5/5 Over a week Newbie friendliness 25/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 3/5 1-2 days Newbie friendliness 38/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Difficulty 5/5 Over a week Newbie friendliness 15/100
-
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 5/5 Over a week Newbie friendliness 25/100
-
Difficulty 2/5 1-3 hours Newbie friendliness 38/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
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
-
Difficulty 2/5 1-3 hours Newbie friendliness 52/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 3/5 1-2 days Newbie friendliness 25/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 3/5 1-2 days Newbie friendliness 25/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Difficulty 5/5 Over a week Newbie friendliness 15/100
-
Difficulty 5/5 Over a week Newbie friendliness 30/100
-
Difficulty 3/5 1-2 days Newbie friendliness 30/100
-
Difficulty 5/5 Over a week Newbie friendliness 20/100
-
Temporal splitting Open
Difficulty 4/5 3-5 days Newbie friendliness 30/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
-
Difficulty 5/5 Over a week Newbie friendliness 15/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
-
Difficulty 4/5 3-5 days Newbie friendliness 28/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
-
Difficulty 4/5 3-5 days Newbie friendliness 25/100
-
Difficulty 5/5 Over a week Newbie friendliness 15/100
-
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
-
Difficulty 5/5 Over a week Newbie friendliness 25/100