Metadata-Version: 2.1
Name: deepctr
Version: 0.5.2
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
Description: # DeepCTR
        
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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 layers which can be used to build your own custom model easily.It is implemented by tensorflow.You can use any complex model with `model.fit()`and `model.predict()` .
        
        Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955))
        
        
        ## Models List
        
        |                 Model                  | Paper                                                                                                                                                           |
        | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        | Convolutional Click Prediction Model | [CIKM 2015][A Convolutional Click Prediction Model](http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf)                    |
        | 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               | [DLRS 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)                                               |
        |                xDeepFM                 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)                         |
        |                AutoInt                 | [arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              |
        |         Deep Interest Network          | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)                                                       |
        |    Deep Interest Evolution Network     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)                                           |
        |                  NFFM       | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                       |
        |                  FGCNN                  | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447)   |
        |                  Deep Session Interest Network                | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482)  |
        |                  FiBiNET               | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)  |
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 :: 2.7
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: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*
Description-Content-Type: text/markdown
Provides-Extra: cpu
Provides-Extra: gpu
