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Recoder\n[![Pypi version](https://img.shields.io/badge/dynamic/json.svg?label=pypi\u0026url=https%3A%2F%2Fpypi.org%2Fpypi%2Frecsys-recoder%2Fjson\u0026query=%24.info.version\u0026colorB=blue)](https://pypi.org/project/recsys-recoder/)\n[![Docs status](https://readthedocs.org/projects/recoder/badge/?version=latest)](https://recoder.readthedocs.io/en/latest/)\n[![Build Status](https://travis-ci.org/amoussawi/recoder.svg?branch=master)](https://travis-ci.org/amoussawi/recoder)\n\n### Introduction\n\nRecoder is a fast implementation for training collaborative filtering latent\nfactor models with mini-batch based negative sampling following recent work:\n- [Towards Large Scale Training Of Autoencoders For Collaborative Filtering](https://arxiv.org/abs/1809.00999).\n\nRecoder contains two implementations of factorization models: Autoencoder and Matrix Factorization.\n\nCheck out the [Documentation](https://recoder.readthedocs.io/en/latest/) and\nthe [Tutorial](https://recoder.readthedocs.io/en/latest/tutorial.html).\n\n### Installation\nRecommended to use python 3.8. Python 2 is not supported.\n\n```bash\npip install -U recsys-recoder\n```\n\n### Examples\nCheck out the `scripts/` directory for some good examples on different datasets.\nYou can get MovieLens-20M dataset fully trained with mean squared error in less\nthan a minute on a Nvidia Tesla K80 GPU.\n\n### Further Readings\n- [Collaborative Filtering for Implicit Feedback Datasets](http://yifanhu.net/PUB/cf.pdf)\n- [Variational Autoencoders for Collaborative Filtering](https://arxiv.org/abs/1802.05814)\n\n### Citing\nPlease cite this paper in your publications if it helps your research:\n```\n@inproceedings{recoder,\n  author = {Moussawi, Abdallah},\n  title = {Towards Large Scale Training Of Autoencoders For Collaborative Filtering},\n  booktitle = {Proceedings of Late-Breaking Results track part of the Twelfth ACM Conference on Recommender Systems},\n  series = {RecSys'18},\n  year = {2018},\n  address = {Vancouver, BC, Canada}\n}\n```\n\n### Acknowledgements\n- I would like to thank [Anghami](https://www.anghami.com) for supporting this work,\nand my colleagues, [Helmi Rifai](https://twitter.com/RifaiHelmi) and\n[Ramzi Karam](https://twitter.com/ramzikaram), for great discussions on Collaborative Filtering at scale.\n\n- This project started as a fork of [NVIDIA/DeepRecommender](https://github.com/NVIDIA/DeepRecommender),\nand although it went in a slightly different direction and was entirely refactored,\nthe work in [NVIDIA/DeepRecommender](https://github.com/NVIDIA/DeepRecommender) was a\ngreat contribution to the work here.\n","funding_links":[],"categories":["Recommendation"],"sub_categories":["Tools"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famoussawi%2Frecoder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famoussawi%2Frecoder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famoussawi%2Frecoder/lists"}