Metadata-Version: 2.1
Name: deepray
Version: 0.1.0
Summary: A new Modular, Scalable, Configurable, Easy-to-Use and Extend infrastructure for Deep Learning based Recommendation.
Home-page: https://github.com/fuhailin/deepray
Author: Hailin Fu
Author-email: hailinfufu@outlook.com
License: Apache-2.0
Description: **DeePray** (`深度祈祷`): A new Modular, Scalable, Configurable, Easy-to-Use and Extend infrastructure for Deep Learning based Recommendation.
        
        Let's Get Started!
        https://deepray.readthedocs.io/en/latest/
        
        Models List
        
        | Titile                                                       |  Booktitle  | Resources                                                    |
        | ------------------------------------------------------------ | :---------: | ------------------------------------------------------------ |
        | **FM**: Factorization Machines                               |  ICDM'2010  | [[pdf]](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_fm.py) |
        | **FFM**: Field-aware Factorization Machines for CTR Prediction | RecSys'2016 | [[pdf]](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_ffm.py) |
        | **FNN**: Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction |  ECIR'2016  | [[pdf]](https://arxiv.org/abs/1601.02376)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_fnn.py) |
        | **PNN**: Product-based Neural Networks for User Response Prediction |  ICDM'2016  | [[pdf]](https://arxiv.org/abs/1611.00144)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_pnn.py) |
        | **Wide&Deep**: Wide & Deep Learning for Recommender Systems  |  DLRS'2016  | [[pdf]](https://arxiv.org/pdf/1606.07792)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_wdl.py) |
        | **AFM**: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | IJCAI'2017  | [[pdf]](https://arxiv.org/abs/1708.04617)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_afm.py) |
        | **NFM**: Neural Factorization Machines for Sparse Predictive Analytics | SIGIR'2017  | [[pdf]](https://arxiv.org/abs/1708.05027)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_nfm.py) |
        | **DeepFM**: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C] | IJCAI'2017  | [[pdf]](https://arxiv.org/abs/1703.04247) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_deepfm.py) |
        | **DCN**: Deep & Cross Network for Ad Click Predictions       | ADKDD'2017  | [[pdf]](https://arxiv.org/abs/1708.05123) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dcn.py) |
        | **xDeepFM**: xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems |  KDD'2018   | [[pdf]](https://arxiv.org/abs/1803.05170) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_xdeepfm.py) |
        | **DIN**: DIN: Deep Interest Network for Click-Through Rate Prediction |  KDD'2018   | [[pdf]](https://arxiv.org/abs/1706.06978) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dien.py) |
        | **DIEN**: DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction |  AAAI'2019  | [[pdf]](https://arxiv.org/abs/1809.03672) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dien.py) |
        | **DSIN**: Deep Session Interest Network for Click-Through Rate Prediction | IJCAI'2019  | [[pdf]](https://arxiv.org/abs/1905.06482)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dsin.py) |
        | **AutoInt**: Automatic Feature Interaction Learning via Self-Attentive Neural Networks |  CIKM'2019  | [[pdf]](https://arxiv.org/abs/1810.11921)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_autoint.py) |
        | **FLEN**: Leveraging Field for Scalable CTR Prediction       |  AAAI'2020  | [[pdf]](https://arxiv.org/pdf/1911.04690.pdf)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_flen.py) |
        | **DFN**: Deep Feedback Network for Recommendation            | IJCAI'2020  | [[pdf]]()[[code]](TODO)                                      |
        
        # How to build your own model with DeePray
        
        Inheriting   `BaseCTRModel` class from `from deepray.model.model_ctr`, and implement your own `build_network()` method!
        
        
        # Contribution
        
        DeePray is still under development, and call for contributions!
        
        ```
        * Hailin Fu (`Hailin <https://github.com/fuhailin>`)
        * Call for contributions!
        ```
        
        # License
        
        Copyright (c) Copyright © 2020 The DeePray Authors<Hailin Fu>. All Rights Reserved.
        
        Licensed under the [GNU](LICENSE) License.
        
        # Contact
        If you have any questions, please follow the following account:
        
        <img src="https://gitee.com/fuhailin/Object-Storage-Service/raw/master/wechat_channel.png" >
Keywords: ctr,recommendation,deep learning,tensorflow,tensor,keras
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software 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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
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: >=3.6
Description-Content-Type: text/markdown
