LLMRec
implicit
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LLMRec
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?
torchrec - Pytorch domain library for recommendation systems
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
recommenders - Best Practices on Recommendation Systems
annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
graph-progression - Create a progression of recommendations from a user-supplied recommender
fastFM - fastFM: A Library for Factorization Machines
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
spotlight - Deep recommender models using PyTorch.
libffm - A Library for Field-aware Factorization Machines
Surprise - A Python scikit for building and analyzing recommender systems
matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering