LightFM VS matrix-factorization

Compare LightFM vs matrix-factorization and see what are their differences.

LightFM

A Python implementation of LightFM, a hybrid recommendation algorithm. (by lyst)

matrix-factorization

Library for matrix factorization for recommender systems using collaborative filtering (by Quang-Vinh)
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LightFM matrix-factorization
0 1
4,068 8
0.7% -
6.2 1.0
4 months ago almost 2 years ago
Python Python
Apache License 2.0 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.

LightFM

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

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

matrix-factorization

Posts with mentions or reviews of matrix-factorization. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-26.

What are some alternatives?

When comparing LightFM and matrix-factorization you can also consider the following projects:

Surprise - A Python scikit for building and analyzing recommender systems

tensorflow - An Open Source Machine Learning Framework for Everyone

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

spotlight - Deep recommender models using PyTorch.

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

MLflow - Open source platform for the machine learning lifecycle

Keras - Deep Learning for humans

Crab - Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).

scikit-learn - scikit-learn: machine learning in Python

gym - A toolkit for developing and comparing reinforcement learning algorithms.

python-recsys - A python library for implementing a recommender system

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.