spotlight
Deep recommender models using PyTorch. (by maciejkula)
implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets (by benfred)
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| spotlight | implicit | |
|---|---|---|
| - | 3 | |
| 2,934 | 3,424 | |
| - | - | |
| 0.0 | 6.2 | |
| over 1 year ago | about 1 month 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.
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.
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.
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
-
Content-based Recommender System with Python
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?
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 spotlight and implicit you can also consider the following projects:
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
fastFM - fastFM: A Library for Factorization Machines
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