implicit
libffm
Our great sponsors
| implicit | libffm | |
|---|---|---|
| 3 | - | |
| 3,424 | 1,594 | |
| - | - | |
| 6.2 | 0.0 | |
| about 1 month ago | about 3 years ago | |
| Python | C++ | |
| MIT License | BSD 3-clause "New" or "Revised" 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.
implicit
- Recommendation system integration
-
Content-based Recommender System with Python
Although CF methods also have some explainability available. CF library https://github.com/benfred/implicit which I used a lot in my past projects, e.g. has the method model.explain available for that.
-
Tensorflow Recommender (TFRS) or Scikit-Surprise?
In that case, you are doing some form of collaborative filtering, though you can also add content-based filtering as additional features later. You can use either implicit or explicit feedback. I would suggest checking this package, and this tutorial. Let me know if you have any other questions.
libffm
We haven't tracked posts mentioning libffm yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
fastFM - fastFM: A Library for Factorization Machines
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
spotlight - Deep recommender models using PyTorch.
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Surprise - A Python scikit for building and analyzing recommender systems