Metadata-Version: 2.1
Name: deepctr
Version: 0.2.0.post1
Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow.
Home-page: https://github.com/shenweichen/deepctr
Author: Weichen Shen
Author-email: wcshen1994@163.com
License: MIT license
Download-URL: https://github.com/shenweichen/deepctr/tags
Keywords: ctr,click through rate,deep learning,tensorflow,tensor,keras
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.4
Description-Content-Type: text/markdown
Provides-Extra: tf
Provides-Extra: tf_gpu
Requires-Dist: tensorflow (!=1.7.*,!=1.8.*,>=1.4.0)
Requires-Dist: h5py
Provides-Extra: tf
Requires-Dist: tensorflow (!=1.7.*,!=1.8.*,>=1.4.0); extra == 'tf'
Provides-Extra: tf_gpu
Requires-Dist: tensorflow-gpu (!=1.7.*,!=1.8.*,>=1.4.0); extra == 'tf_gpu'

# DeepCTR

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)](https://github.com/shenweichen/deepctr/issues)
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[![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/)
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DeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based CTR models along with lots of core components layer  which can be used to build your own custom model easily.You can use any complex model with `model.fit()`and`model.predict()` .And the layers are compatible with tensorflow.

Through  `pip install deepctr`  get the package and [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)


## Models List

|Model|Paper|
|:--:|:--|
|Factorization-supported Neural Network|[ECIR 2016][Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf)|
|Product-based Neural Network|[ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf)|
|Wide & Deep|[arxiv 2016][Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf)|
|DeepFM|[IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf)|
|Piece-wise Linear Model|[arxiv 2017][Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/abs/1704.05194)|
|Deep & Cross Network|[ADKDD 2017][Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123)|
|Attentional Factorization Machine|[IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435)|
|Neural Factorization Machine|[SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf)|
|Deep Interest Network|[KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)|
|xDeepFM|[KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)|


