libffm vs implicit

libffm

A Library for Field-aware Factorization Machines (by ycjuan)

implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets (by benfred)

Recommender Systems collaborative-filtering Machine Learning matrix-factorization recommender-system recommendation-system Recommendation

Source Code

benfred.github.io

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libffm		implicit
	Project
-	Mentions	3
1,594	Stars	3,420
-	Growth	-
0.0	Activity	6.2
about 3 years ago	Latest Commit	about 1 month ago
C++	Language	Python
BSD 3-clause "New" or "Revised" License	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.

libffm

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

We haven't tracked posts mentioning libffm 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.

Recommendation system integration
1 project | /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 | /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 libffm and implicit you can also consider the following projects:

fastFM - fastFM: A Library for Factorization Machines

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

spotlight - Deep recommender models using PyTorch.

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

RecBole - A unified, comprehensive and efficient recommendation library

Surprise - A Python scikit for building and analyzing recommender systems