Factorization Machine models in PyTorch

This package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction.

Available Datasets

• MovieLens Dataset
• Criteo Display Advertising Challenge
• Avazu Click-Through Rate Prediction

Available Models

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, .

Each model's AUC values are about 0.80 for criteo dataset, and about 0.78 for avazu dataset. (please see example code)

Installation

pip install torchfm

API Documentation

https://rixwew.github.io/pytorch-fm

Licence

MIT