Large scale training of factorization models for Collaborative Filtering with PyTorch
Alternatives To Recoder
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
2 years ago22mitPython
SincNet is a neural architecture for efficiently processing raw audio samples.
Neural Collaborative Filtering312
4 years ago5Jupyter Notebook
pytorch version of neural collaborative filtering
a day ago20June 05, 202220mitPython
Versatile End-to-End Recommender System
3 years ago2mitPython
PyTorch implementation of the wavelet analysis from Torrence & Compo (1998)
5 years agoPython
Singing Voice Separation via Recurrent Inference and Skip-Filtering Connections - PyTorch Implementation. Demo:
3 years ago7May 27, 20201mitPython
rectorch is a pytorch-based framework for state-of-the-art top-N recommendation
3 years ago3Python
A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17
Lr Gccf69
2 years ago1Python
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020
Ngcf Pytorch57
2 years ago1Python
PyTorch Implementation for Neural Graph Collaborative Filtering
Vae Cf Pytorch53
2 years ago1Python
Variational Autoencoders for Collaborative Filtering - Implementation in PyTorch
Alternatives To Recoder
Select To Compare

Alternative Project Comparisons


Pypi version Docs status Build Status


Recoder is a fast implementation for training collaborative filtering latent factor models with mini-batch based negative sampling following recent work:

Recoder contains two implementations of factorization models: Autoencoder and Matrix Factorization.

Check out the Documentation and the Tutorial.


Recommended to use python 3.8. Python 2 is not supported.

pip install -U recsys-recoder


Check out the scripts/ directory for some good examples on different datasets. You can get MovieLens-20M dataset fully trained with mean squared error in less than a minute on a Nvidia Tesla K80 GPU.

Further Readings


Please cite this paper in your publications if it helps your research:

  author = {Moussawi, Abdallah},
  title = {Towards Large Scale Training Of Autoencoders For Collaborative Filtering},
  booktitle = {Proceedings of Late-Breaking Results track part of the Twelfth ACM Conference on Recommender Systems},
  series = {RecSys'18},
  year = {2018},
  address = {Vancouver, BC, Canada}


  • I would like to thank Anghami for supporting this work, and my colleagues, Helmi Rifai and Ramzi Karam, for great discussions on Collaborative Filtering at scale.

  • This project started as a fork of NVIDIA/DeepRecommender, and although it went in a slightly different direction and was entirely refactored, the work in NVIDIA/DeepRecommender was a great contribution to the work here.

Popular Pytorch Projects
Popular Filtering Projects
Popular Machine Learning Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Machine Learning
Deep Learning
Recommender System
Collaborative Filtering
Matrix Factorization