Metadata-Version: 2.1
Name: deep-rec
Version: 0.0.4.dev1
Summary: PyTorch implementation of Deep Factorization Machine Models
Home-page: https://github.com/jaisenbe58r/deep-rec
Author: Jaime Sendra Berenguer
Author-email: jaimesendraberenguer@gmail.com
License: UNKNOWN
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: wandb (==0.10.27)
Requires-Dist: tqdm
Requires-Dist: scikit-learn
Requires-Dist: setuptools

# Factorization Machine models in PyTorch

This package provides a PyTorch implementation of Deep Factorization Machine ;odels and common datasets in Retail Recommendation.


## Available Datasets

* [Retail Case Study Data](https://www.kaggle.com/darpan25bajaj/retail-case-study-data/download)


## Available Models

| Model | Reference |
|-------|-----------|
| DeepFM | [H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.](https://arxiv.org/abs/1703.04247) |
| xDeepFM | [J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018.](https://arxiv.org/abs/1803.05170) |


## Environment


    conda env create -f environment_conda.yml
    source activate environment_conda


## Installation

    pip install deep-rec

## Example

    python main.py --device cpu --epoch 2 

## API Documentation

https://rixwew.github.io/deep-rec (en construcción)


## Licence

MIT

