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PyTorch implementation of Factorization Machine Models

Project description

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 Factorization Machines
Field-aware Factorization Machine Field-aware Factorization Machines for CTR Prediction
Factorization-Supported Neural Network Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction
Wide&Deep Wide & Deep Learning for Recommender Systems
Attentional Factorization Machine Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Neural Factorization Machine Neural Factorization Machines for Sparse Predictive Analytics
Field-aware Neural Factorization Machine Field-aware Neural Factorization Machine for Click-Through Rate Prediction
Product Neural Network Product-based Neural Networks for User Response Prediction
Deep Cross Network Deep & Cross Network for Ad Click Predictions
DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
xDeepFM xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
AutoInt (Automatic Feature Interaction Model) AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

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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