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.
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 )
pip install torchfm
https://rixwew.github.io/pytorch-fm
MIT
