DeepFM_with_PyTorch
所属分类：其他
开发工具：Python
文件大小：40KB
下载次数：4
上传日期：2020-04-12 11:04:21
上 传 者：
龙的独白式
说明： 推荐系统 deepFM python实现，用于对CTR预测
(Implementation of deepfm python, a recommendation system for CTR prediction)
文件列表:
DeepFM_with_PyTorch\.idea\DeepFM_with_PyTorch.iml (464, 2020-01-09)
DeepFM_with_PyTorch\.idea\misc.xml (308, 2020-01-09)
DeepFM_with_PyTorch\.idea\modules.xml (297, 2020-01-09)
DeepFM_with_PyTorch\.idea\vcs.xml (185, 2020-01-09)
DeepFM_with_PyTorch\.idea\workspace.xml (13967, 2020-01-09)
DeepFM_with_PyTorch\main.py (1037, 2020-01-09)
DeepFM_with_PyTorch\model\DeepFM.py (7267, 2020-01-09)
DeepFM_with_PyTorch\model\__init__.py (0, 2020-01-09)
DeepFM_with_PyTorch\model\__pycache__\DeepFM.cpython-37.pyc (6129, 2020-01-09)
DeepFM_with_PyTorch\model\__pycache__\__init__.cpython-37.pyc (146, 2020-01-09)
DeepFM_with_PyTorch\utils\dataPreprocess.py (6155, 2020-01-09)
DeepFM_with_PyTorch\utils\__init__.py (0, 2020-01-09)
... ...
# 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/
近期下载者：
相关文件：
收藏者：