Factorization Machine models in PyTorch

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

Available Datasets

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.

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

MIT

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for torchfm 0.6
File	Size	Uploaded
torchfm-0.6.tar.gz	8.7 kB	Sep 25, 2019	Details

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