matrix-factorization VS spotlight

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

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matrix-factorization spotlight
1 0
19 2,934
- -
2.9 0.0
6 months ago over 1 year ago
Python Python
MIT 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.

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.

spotlight

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

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

What are some alternatives?

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

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.

fastFM - fastFM: A Library for Factorization Machines

implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets

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

PERSIA - High performance distributed framework for training deep learning recommendation models based on PyTorch.

ranking - Learning to Rank in TensorFlow

fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production