py-dynacli
recommenders
| py-dynacli | recommenders | |
|---|---|---|
| 3 | 6 | |
| 99 | 17,980 | |
| - | 1.0% | |
| 0.0 | 9.5 | |
| about 1 year ago | 10 days ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
py-dynacli
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This Week in Python
py-dynacli – cloud-friendly Python library for converting pure Python functions into Linux Shell commands
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An easy way to convert unlimited python functions into shell commands - a QR example
You can check more about Enum type support in our test cases: https://github.com/BstLabs/py-dynacli/tree/main/test/integrated/storage_F/cli/dev/feature_A
and here’s the GitHub repo: https://github.com/BstLabs/py-dynacli
recommenders
- My kernel dies when I fit my LightFm model from Microsoft Recommenders
- There is framework for everything.
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This Week in Python
recommenders – Best Practices on Recommendation Systems
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Input to SVD, SAR, NMF
I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
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Opinion on choice of model - Recommender System
Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.
What are some alternatives?
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