implicit VS LightFM

Compare implicit vs LightFM and see what are their differences.

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implicit LightFM
3 -
3,420 4,593
- 0.7%
6.2 4.8
about 1 month ago 4 months ago
Python Python
MIT License Apache License 2.0
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.

implicit

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

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.

What are some alternatives?

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

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

Surprise - A Python scikit for building and analyzing recommender systems

fastFM - fastFM: A Library for Factorization Machines

tensorflow - An Open Source Machine Learning Framework for Everyone

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

MLflow - Open source platform for the machine learning lifecycle

RecBole - A unified, comprehensive and efficient recommendation library

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

spotlight - Deep recommender models using PyTorch.

libffm - A Library for Field-aware Factorization Machines

Keras - Deep Learning for humans