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Factorization Machine models in PyTorch

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Factorization Machine models in PyTorch

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

Available Datasets

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. | | 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

API Documentation



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