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