|over 1 year ago||6 days ago|
|MIT License||MIT License|
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Recommendation system integration
1 project | reddit.com/r/django | 31 Mar 2022
Content-based Recommender System with Python
1 project | dev.to | 4 Jan 2022
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?
1 project | reddit.com/r/deeplearning | 24 Jan 2021
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?
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
fastFM - fastFM: A Library for Factorization Machines
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
libffm - A Library for Field-aware Factorization Machines
matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering