fastFM VS implicit

Compare fastFM vs implicit and see what are their differences.

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fastFM implicit
0 3
984 2,782
- -
0.0 8.3
6 months ago 5 days ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

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.

What are some alternatives?

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

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

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

libffm - A Library for Field-aware Factorization Machines

RecBole - A unified, comprehensive and efficient recommendation library

spotlight - Deep recommender models using PyTorch.

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

Surprise - A Python scikit for building and analyzing recommender systems