matrix-factorization
Library for matrix factorization for recommender systems using collaborative filtering (by Quang-Vinh)
LT-OCF
LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21 (by jeongwhanchoi)
| matrix-factorization | LT-OCF | |
|---|---|---|
| 1 | 1 | |
| 21 | 35 | |
| - | - | |
| 2.9 | 3.1 | |
| over 1 year ago | 10 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.
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.
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Recently launched my first end-to-end ML app! A film recommender system based on matrix factorization, built for Letterboxd users.
I originally tried using RiverML, which is dedicated to online ML, but after a ton of tweaking I still wasn't satisfied. In the end, I used the matrix-factorization library, which is not at all flashy but worked much, much better. By adjusting the learning rate and epochs for feeding new ratings into the model I can adjust how "personalized" the ratings are, and after a few days of messing with it I got it where I wanted it.
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.
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[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)!
What are some alternatives?
When comparing matrix-factorization and LT-OCF you can also consider the following projects:
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
HMLET - Linear, or Non-Linear, That is the Question!, WSDM'22
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
movie-recommender - Movie recommender system based on Non-Negative Matrix Factorization and Singular Value Decomposition, with a Flask web interface
riscv-newop - A RISC-V new instruction discovery tool [Work in Progress]
Federated-Recommendation-Neural-Collaborative-Filtering - Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.