Pytorch package for training factorization models for collaborative filtering

Hello community,

I would like to share a package built with pytorch to train recommendation system models

It contains two factorization models implementations: (Deep) Autoencoders and Matrix Factorization. You can build your own factorization model and train it.

It was built to be fast for large-scale training with negative sampling, for instance you can have an Autoencoder model fully trained on Movielens-20M dataset in less than a minute on a Tesla K80 GPU.

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