Factorization Machine models in PyTorch
This package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction.
| Model |
Reference |
| Logistic Regression |
|
| Factorization Machine |
S Rendle, Factorization Machines, 2010. |
| Field-aware Factorization Machine |
Y Juan, et al. Field-aware Factorization Machines for CTR Prediction, 2015. |
| Higher-Order Factorization Machines |
M Blondel, et al. Higher-Order Factorization Machines, 2016. |
| Factorization-Supported Neural Network |
W Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016. |
| Wide&Deep |
HT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016. |
| Attentional Factorization Machine |
J Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017. |
| Neural Factorization Machine |
X He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017. |
| Neural Collaborative Filtering |
X He, et al. Neural Collaborative Filtering, 2017. |
| Field-aware Neural Factorization Machine |
L Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019. |
| Product Neural Network |
Y Qu, et al. Product-based Neural Networks for User Response Prediction, 2016. |
| Deep Cross Network |
R Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017. |
| DeepFM |
H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017. |
| xDeepFM |
J Lian, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems, 2018. |
| AutoInt (Automatic Feature Interaction Model) |
W Song, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2018. |
| AFN(AdaptiveFactorizationNetwork Model) |
Cheng W, et al. Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions, AAAI'20. |
Each model's AUC values are about 0.80 for criteo dataset, and about 0.78 for avazu dataset. (please see example code)
https://rixwew.github.io/pytorch-fm
MIT