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

MIT

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

Table of built distributions (wheels) for torchfm 0.2
File	Interpreter	ABI	Platform	Reset
torchfm-0.2-py3-none-any.whl 16.9 kB May 28, 2019	Python 3	none	any	Details

Download URL	torchfm-0.2-py3-none-any.whl
Size	16.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`f06ea57fa8bb69fe77b6515fea5ffd7642b8cb0663adc6ef35740970c554c1f7`
BLAKE2b-256 checksum How to use checksums	`84352e94437b55f1ae0350e98449212f5fa8a7dd91ff08bf54617ae71be351ab`
Upload date	May 28, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3`