LightFM vs implicit

LightFM

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

#Machine Learning #Recommender #Python #matrix-factorization #learning-to-rank #recommender-system

Source Code

implicit

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

#Recommender Systems #collaborative-filtering #Machine Learning #matrix-factorization #recommender-system

Source Code

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LightFM		implicit
	Project
0	Mentions	3
4,057	Stars	2,819
1.5%	Growth	-
6.4	Activity	8.2
3 months ago	Latest Commit	9 days ago
Python	Language	Python
MIT 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.

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.

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 | reddit.com/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 | 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 LightFM and implicit 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

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

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

fastFM - fastFM: A Library for Factorization Machines

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

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).

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

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

LightFM vs Surprise

LightFM vs tensorflow

implicit vs TensorRec

LightFM vs Crab