|3 days ago||25 days ago|
|MIT License||MIT License|
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.
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.
We haven't tracked posts mentioning spotlight yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
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
fastFM - fastFM: A Library for Factorization Machines
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
RecBole - A unified, comprehensive and efficient recommendation library
libffm - A Library for Field-aware Factorization Machines
Surprise - A Python scikit for building and analyzing recommender systems