# DeepCTR **Repository Path**: Litra/DeepCTR ## Basic Information - **Project Name**: DeepCTR - **Description**: Easy-to-use,Modular and Extendible package of deep-learning based CTR models for search and recommendation. - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-06-13 - **Last Updated**: 2021-06-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DeepCTR [![Python Versions](https://img.shields.io/pypi/pyversions/deepctr.svg)](https://pypi.org/project/deepctr) [![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-1.4+/2.0+-blue.svg)](https://pypi.org/project/deepctr) [![Downloads](https://pepy.tech/badge/deepctr)](https://pepy.tech/project/deepctr) [![PyPI Version](https://img.shields.io/pypi/v/deepctr.svg)](https://pypi.org/project/deepctr) [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr.svg )](https://github.com/shenweichen/deepctr/issues) [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) [![Build Status](https://travis-ci.org/shenweichen/DeepCTR.svg?branch=master)](https://travis-ci.org/shenweichen/DeepCTR) [![Coverage Status](https://coveralls.io/repos/github/shenweichen/DeepCTR/badge.svg?branch=master)](https://coveralls.io/github/shenweichen/DeepCTR?branch=master) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/app/wcshen1994/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE) 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 easily build custom models.You can use any complex model with `model.fit()`，and `model.predict()` . - Provide `tf.keras.Model` like interface for **quick experiment**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr) - Provide `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord) - It is compatible with both `tf 1.x` and `tf 2.x`. Some related project: - DeepMatch: https://github.com/shenweichen/DeepMatch - DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955)) and [welcome to join us!](./CONTRIBUTING.md) ## 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) | | FwFM | [WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) | | ONN | [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) | | FLEN | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf) | ## Citation - Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr. If you find this code useful in your research, please cite it using the following BibTeX: ```bibtex @misc{shen2017deepctr, author = {Weichen Shen}, title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models}, year = {2017}, publisher = {GitHub}, journal = {GitHub Repository}, howpublished = {\url{https://github.com/shenweichen/deepctr}}, } ``` ## DisscussionGroup 交流群 Please follow our wechat to join group: - 公众号：**浅梦的学习笔记** - wechat ID: **deepctrbot** ![wechat](./docs/pics/code.png) ## Cooperative promotion 合作推广 For more information about the recommendation system, such as **feature engineering, user profile, matching, ranking and multi-objective optimization, online learning and real-time computing, and more cutting-edge technologies and practical projects**: 更多关于推荐系统的内容，如**特征工程，用户画像，召回，排序和多目标优化，在线学习与实时计算以及更多前沿技术和实战项目**等可参考： - [推荐系统实战](https://www.julyedu.com/course/getDetail/181?ccode=5ee751d37278c) - [互联网计算广告实战](https://www.julyedu.com/course/getDetail/158?ccode=5ee751d37278c)