codeants2012 / torecsys

ToR[e]cSys is a package which implementing famous recommendation system algorithm in PyTorch, including Click-through-rate prediction, Learning-to-ranking, or embedding.

ToR[e]cSys

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ToR[e]cSys is a Python package which implementing famous recommendation system
algorithm in PyTorch, including Click-through-rate prediction, Learning-to-ranking,
and Items Embedding.

• Installation
• Implemented Models
• Documentation
• More About ToR[e]cSys
• Getting Started
• Examples
• Authors
• License

Installation

By pip package

pip install torecsys

From source

git clone https://github.com/p768lwy3/torecsys.git
python setup.py build
python setup.py install

Build Documentation

git clone https://github.com/p768lwy3/torecsys.git
cd ./torecsys/doc
./make html

Documentation

The complete documentation for ToR[e]cSys is avaiable via ReadTheDocs website.
Thank you for ReadTheDocs!!!

Implemented Models

Model Name	Research Paper	Type
Attentional Factorization Machine	Jun Xiao et al, 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	Click Through Rate
Deep and Cross Network	Ruoxi Wang et al, 2017. Deep & Cross Network for Ad Click Predictions	Click Through Rate
Deep Field-Aware Factorization Machine	Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine	Click Through Rate
Deep Factorization Machine	Huifeng Guo et al, 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	Click Through Rate
Deep Matching Correlation Prediction	Wentao Ouyang et al, 2019. Representation Learning-Assisted Click-Through Rate Prediction	Click Through Rate
Factorization Machine	Steffen Rendle, 2010. Factorization Machine	Click Through Rate
Factorization Machine Support Neural Network	Weinan Zhang et al, 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction	Click Through Rate
Field-Aware Factorization Machine	Yuchin Juan et al, 2016. Field-aware Factorization Machines for CTR Prediction	Click Through Rate
Field-Aware Neural Factorization Machine	Li Zhang et al, 2019. Field-aware Neural Factorization Machine for Click-Through Rate Prediction	Click Through Rate

More About ToR[e]cSys

Component	Description
[torecsys.data]	download sample data, build dataloader, and other functions for convenience
[torecsys.estimators]	models with embedding, which can be trained with .fit(dataloader) directly
[torecsys.functional]	functions used in recommendation system
[torecsys.inputs]	inputs' functions, including embedding, image transformations
[torecsys.layers]	layers-level implementation of algorithms
[torecsys.losses]	loss functions used in recommendation system
[torecsys.metrics]	metrics to evaluate recommendation system
[torecsys.models]	whole-architecture of models which can be trained by torecsys.base.trainer
[torecsys.utils]	little tools used in torecsys

(!!! To be confirmed)

torecsys.models

torecsys.models is a part of model excluding embedding part, so you can choose
a suitable embedding method for your model with the following codes:

torecsys.estimators

torecsys.estimators is another type of model to be used directly if the input
fields and features implemented in the papers are suitable for you:

Getting Started

(!!! To be confirmed)

Load Sample data

load the movielens dataset, for example:

Build Dataset and DataLoader with Sample data

Use Estimators to train a model

Make prediction with estimators

Examples

Authors

• Jasper Li - Developer

License

ToR[e]cSys is MIT-style licensed, as found in the LICENSE file.