Skip to content
/ torecsys Public
forked from p768lwy3/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.

License

MIT license
0 stars 17 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

swimmingCreative/torecsys

BranchesTags
 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

162 Commits

doc

doc

 
 

example

example

 
 

torecsys

torecsys

 
 

.gitignore

.gitignore

 
 

LICENSE

LICENSE

 
 

README.md

README.md

 
 

TODO.md

TODO.md

 
 

requirements.txt

requirements.txt

 
 

setup.py

setup.py

 
 

Repository files navigation

ToR[e]cSys

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

TBU

Documentation

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

Implemented Models

1. Subsampling

Model Name Research Paper
Word2Vec Omer Levy et al, 2015. Improving Distributional Similarity with Lessons Learned from Word Embeddings

2. Negative Sampling

Model Name Research Paper
TBU

3. Click through Rate (CTR) Model

Model Name Research Paper
Attentional Factorization Machine Jun Xiao et al, 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Deep and Cross Network Ruoxi Wang et al, 2017. Deep & Cross Network for Ad Click Predictions
Deep Field-Aware Factorization Machine Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Deep Factorization Machine Huifeng Guo et al, 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Deep Matching Correlation Prediction Wentao Ouyang et al, 2019. Representation Learning-Assisted Click-Through Rate Prediction
Elaborated Entire Space Supervised Multi Task Model Hong Wen et al, 2019. Conversion Rate Prediction via Post-Click Behaviour Modeling
Entire Space Multi Task Model Xiao Ma et al, 2019. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Factorization Machine Steffen Rendle, 2010. Factorization Machine
Factorization Machine Support Neural Network Weinan Zhang et al, 2016. Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
Field Attentive Deep Field Aware Factorization Machine Junlin Zhang et al, 2019. FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Field-Aware Factorization Machine Yuchin Juan et al, 2016. Field-aware Factorization Machines for CTR Prediction
Logistic Regression /
Neural Collaborative Filtering Xiangnan He, 2017. Neural Collaborative Filtering
Neural Factorization Machine Xiangnan He et al, 2017. Neural Factorization Machines for Sparse Predictive Analytics
Product Neural Network Yanru QU, 2016. Product-based Neural Networks for User Response Prediction
eXtreme Deep Factorization Machine Jianxun Lian et al, 2018. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Deep Session Interest Network Yufei Feng, 2019. Deep Session Interest Network for Click-Through Rate Prediction
Positon-bias aware learning framework PAL: a position-bias aware learning framework for CTR prediction in live recommender systems

4. Embedding Model

Model Name Research Paper
Matrix Factorization /
Starspace Ledell Wu et al, 2017 StarSpace: Embed All The Things!

5. Learning-to-Rank (LTR) Model

Model Name Research Paper
Personalized Re-ranking Model Personalized Re-ranking for Recommendation

Getting Started

TBU

Examples

TBU

Authors

License

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

About

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

Resources

Readme

License

MIT license
Activity

Stars

0 stars

Watchers

2 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Python 96.5%
  • Jupyter Notebook 3.5%