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Factorization Machine models in PyTorch

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rixwew/pytorch-fm

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

Available Datasets

Available Models

ModelReference
Logistic Regression
Factorization MachineS Rendle, Factorization Machines, 2010.
Field-aware Factorization MachineY 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 NetworkW Zhang, et al. Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, 2016.
Wide&DeepHT Cheng, et al. Wide & Deep Learning for Recommender Systems, 2016.
Attentional Factorization MachineJ Xiao, et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, 2017.
Neural Factorization MachineX He and TS Chua, Neural Factorization Machines for Sparse Predictive Analytics, 2017.
Neural Collaborative FilteringX He, et al. Neural Collaborative Filtering, 2017.
Field-aware Neural Factorization MachineL Zhang, et al. Field-aware Neural Factorization Machine for Click-Through Rate Prediction, 2019.
Product Neural NetworkY Qu, et al. Product-based Neural Networks for User Response Prediction, 2016.
Deep Cross NetworkR Wang, et al. Deep & Cross Network for Ad Click Predictions, 2017.
DeepFMH Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.
xDeepFMJ 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 seeexample code)

Installation

pip install torchfm

API Documentation

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

Licence

MIT

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