LT-OCF
LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21 (by jeongwhanchoi)
implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets (by benfred)
LT-OCF | implicit | |
---|---|---|
1 | 3 | |
20 | 3,435 | |
- | - | |
10.0 | 6.2 | |
over 1 year ago | about 2 months 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.
LT-OCF
Posts with mentions or reviews of LT-OCF.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-07.
-
[R] Blurring-Sharpening Process Models for Collaborative Filtering (TLDR: graph filtering-based methods + inspired by SGMs = SOTA models for recommender systems)
We have more exciting works on recommender systems; please check our LT-OCF (code) and HMLET (code)!
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.
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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.