https://github.com/amoussawi/recoder

Large scale training of factorization models for Collaborative Filtering with PyTorch
https://github.com/amoussawi/recoder

autoencoder collaborative-filtering deep-learning machine-learning matrix-factorization pytorch recommender-system

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Large scale training of factorization models for Collaborative Filtering with PyTorch

• Host: GitHub
• URL: https://github.com/amoussawi/recoder
• Owner: amoussawi
• License: mit
• Created: 2018-02-05T20:44:21.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T22:37:45.000Z (12 months ago)
• Last Synced: 2024-03-02T11:37:33.298Z (4 months ago)
• Topics: autoencoder, collaborative-filtering, deep-learning, machine-learning, matrix-factorization, pytorch, recommender-system
• Language: Python
• Homepage:
• Size: 3.47 MB
• Stars: 56
• Watchers: 5
• Forks: 7
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-embeddings - Recoder

README

        
# Recoder

[![Pypi version](https://img.shields.io/badge/dynamic/json.svg?label=pypi&url=https%3A%2F%2Fpypi.org%2Fpypi%2Frecsys-recoder%2Fjson&query=%24.info.version&colorB=blue)](https://pypi.org/project/recsys-recoder/)

[![Docs status](https://readthedocs.org/projects/recoder/badge/?version=latest)](https://recoder.readthedocs.io/en/latest/)

[![Build Status](https://travis-ci.org/amoussawi/recoder.svg?branch=master)](https://travis-ci.org/amoussawi/recoder)

### Introduction

Recoder is a fast implementation for training collaborative filtering latent

factor models with mini-batch based negative sampling following recent work:

- [Towards Large Scale Training Of Autoencoders For Collaborative Filtering](https://arxiv.org/abs/1809.00999).

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

Check out the [Documentation](https://recoder.readthedocs.io/en/latest/) and

the [Tutorial](https://recoder.readthedocs.io/en/latest/tutorial.html).

### Installation

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

```bash

pip install -U recsys-recoder

```

### Examples

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

- [Collaborative Filtering for Implicit Feedback Datasets](http://yifanhu.net/PUB/cf.pdf)

- [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/abs/1802.05814)

### Citing

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

```

@inproceedings{recoder,

  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}

}

```

### Acknowledgements

- I would like to thank [Anghami](https://www.anghami.com) for supporting this work,

and my colleagues, [Helmi Rifai](https://twitter.com/RifaiHelmi) and

[Ramzi Karam](https://twitter.com/ramzikaram), for great discussions on Collaborative Filtering at scale.

- This project started as a fork of [NVIDIA/DeepRecommender](https://github.com/NVIDIA/DeepRecommender),

and although it went in a slightly different direction and was entirely refactored,

the work in [NVIDIA/DeepRecommender](https://github.com/NVIDIA/DeepRecommender) was a

great contribution to the work here.