federeco
implementation of federated neural collaborative filtering algorithm (by Ach113)
recommenders
Best Practices on Recommendation Systems (by recommenders-team)
federeco | recommenders | |
---|---|---|
1 | 6 | |
9 | 17,980 | |
- | 1.0% | |
7.2 | 9.5 | |
10 months ago | 11 days ago | |
Python | Python | |
MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
federeco
Posts with mentions or reviews of federeco.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Federated neural collaborative filtering recommendation system
Implementation of federated recommendation system based on a neural collaborative filtering model - federeco.
recommenders
Posts with mentions or reviews of recommenders.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-04.
- 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.