LightFM
implicit
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| LightFM | implicit | |
|---|---|---|
| - | 3 | |
| 4,593 | 3,420 | |
| 0.7% | - | |
| 4.8 | 6.2 | |
| 4 months ago | about 1 month ago | |
| Python | Python | |
| MIT License | MIT License |
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LightFM
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Tracking mentions began in Dec 2020.
implicit
- Recommendation system integration
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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.
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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.
What are some alternatives?
Surprise - A Python scikit for building and analyzing recommender systems
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
tensorflow - An Open Source Machine Learning Framework for Everyone
fastFM - fastFM: A Library for Factorization Machines
MLflow - Open source platform for the machine learning lifecycle
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
RecBole - A unified, comprehensive and efficient recommendation library
spotlight - Deep recommender models using PyTorch.
Keras - Deep Learning for humans
libffm - A Library for Field-aware Factorization Machines