# DeepFM_with_PyTorch

**Repository Path**: Python_Ai_Road/DeepFM_with_PyTorch

## Basic Information

- **Project Name**: DeepFM_with_PyTorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-04-16
- **Last Updated**: 2022-04-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepFM_with_PyTorch

A PyTorch implementation of DeepFM for CTR prediction problem.

## Usage

1. Download Criteo's Kaggle display advertising challenge dataset from [here][1]( if you have had it already, skip it ), and put it in *./data/raw/*

2. Generate a preprocessed dataset.

        ./utils/dataPreprocess.py

3. Train a model and predict.

        ./main.py

## Output


## Reference

- https://github.com/nzc/dnn_ctr.

- https://github.com/PaddlePaddle/models/tree/develop/deep_fm.

- DeepFM: A Factorization-Machine based Neural Network for CTR         Prediction, Huifeng Guo, Ruiming Tang, Yunming Yey, Zhenguo Li, Xiuqiang He.



[1]: http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/