Factorization Machine models in PyTorch
This package provides a PyTorch implementation of factorization machine models and common datasets in CTR prediction.
||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.
||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.
||H Guo, et al. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, 2017.
||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
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