fastFM VS implicit

Compare fastFM vs implicit and see what are their differences.

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fastFM implicit
0 2
958 2,591
- -
1.8 8.0
about 2 months ago 7 days ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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fastFM

Posts with mentions or reviews of fastFM. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning fastFM yet.
Tracking mentions began in Dec 2020.

implicit

Posts with mentions or reviews of implicit. We have used some of these posts to build our list of alternatives and similar projects.
  • 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?

When comparing fastFM and implicit you can also consider the following projects:

libffm - A Library for Field-aware Factorization Machines

annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

spotlight - Deep recommender models using PyTorch.

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

RecBole - A unified, comprehensive and efficient recommendation library

matrix-factorization - Library for matrix factorization for recommender systems using collaborative filtering