{ "info": { "author": "Weichen Shen", "author_email": "wcshen1994@163.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "# DeepCTR-Torch\n\n[![Python Versions](https://img.shields.io/pypi/pyversions/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch)\n[![Downloads](https://pepy.tech/badge/deepctr-torch)](https://pepy.tech/project/deepctr-torch)\n[![PyPI Version](https://img.shields.io/pypi/v/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch)\n[![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr-torch.svg\n)](https://github.com/shenweichen/deepctr-torch/issues)\n\n\n[![Documentation Status](https://readthedocs.org/projects/deepctr-torch/badge/?version=latest)](https://deepctr-torch.readthedocs.io/)\n![CI status](https://github.com/shenweichen/deepctr-torch/workflows/CI/badge.svg)\n[![codecov](https://codecov.io/gh/shenweichen/DeepCTR-Torch/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR-Torch)\n[![Gitter](https://badges.gitter.im/DeepCTR/community.svg)](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n[![License](https://img.shields.io/github/license/shenweichen/deepctr-torch.svg)](https://github.com/shenweichen/deepctr-torch/blob/master/LICENSE)\n\nPyTorch version of [DeepCTR](https://github.com/shenweichen/DeepCTR).\n\nDeepCTR 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.You can use any complex model with `model.fit()`and `model.predict()` .Install through `pip install -U deepctr-torch`.\n\nLet's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955))\n\n## Contributors([welcome to join us!](./CONTRIBUTING.md))\n\n \"pic\"\n\n\n \"pic\"\n\n\n \"pic\"\n\n\n \"pic\"\n\n\n \"pic\"\n\n\n \"pic\"\n\n\n \"pic\"\n\n\n\n## Models List\n\n| Model | Paper |\n| :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| 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) |\n| 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) |\n| Product-based Neural Network | [ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf) |\n| Wide & Deep | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) |\n| DeepFM | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf) |\n| 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) |\n| Deep & Cross Network | [ADKDD 2017][Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) |\n| Attentional Factorization Machine | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) |\n| Neural Factorization Machine | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) |\n| xDeepFM | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) |\n| AutoInt | [arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) |\n| ONN | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) |\n| FiBiNET | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) |\n\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "https://github.com/shenweichen/deepctr-torch/tags", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/shenweichen/deepctr-torch", "keywords": "ctr,click through rate,deep learning,torch,tensor,pytorch,deepctr", "license": "Apache-2.0", "maintainer": "", "maintainer_email": "", "name": 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