FuxiCTR

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Click-through rate (CTR) prediction is a critical task for various industrial applications such as online advertising, recommender systems, and sponsored search. FuxiCTR provides an open-source library for CTR prediction, with key features in configurability, tunability, and reproducibility. We hope this project could promote reproducible research and benefit both researchers and practitioners in this field. ## Key Features + **Configurable**: Both data preprocessing and models are modularized and configurable. + **Tunable**: Models can be automatically tuned through easy configurations. + **Reproducible**: All the benchmarks can be easily reproduced. + **Extensible**: It can be easily extended to any new models, supporting both Pytorch and Tensorflow frameworks. ## Model Zoo | No | Publication | Model | Paper | Benchmark | Version | |:---:|:-----------------:|:----------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |:---------------------------------------------------------------------------------------------------------------:|:-------------:| |:open_file_folder: **Feature Interaction Models**| | 1 | WWW'07 | [LR](./model_zoo/LR) | [Predicting Clicks: Estimating the Click-Through Rate for New Ads](https://dl.acm.org/citation.cfm?id=1242643) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/LR) | `torch` | | 2 | ICDM'10 | [FM](./model_zoo/FM) | [Factorization Machines](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FM) | `torch` | | 3 | CIKM'13 | [DSSM](./model_zoo/DSSM) | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data ](https://posenhuang.github.io/papers/cikm2013_DSSM_fullversion.pdf) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DSSM) | `torch` | | 4 | CIKM'15 | [CCPM](./model_zoo/CCPM) | [A Convolutional Click Prediction Model](http://www.escience.cn/system/download/73676) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/CCPM) | `torch` | | 5 | RecSys'16 | [FFM](./model_zoo/FFM) | [Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/citation.cfm?id=2959134) :triangular_flag_on_post:**Criteo** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FFM) | `torch` | | 6 | RecSys'16 | [DNN](./model_zoo/DNN) | [Deep Neural Networks for YouTube Recommendations](http://art.yale.edu/file_columns/0001/1132/covington.pdf) :triangular_flag_on_post:**Google** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DNN) | `torch`, `tf` | | 7 | DLRS'16 | [Wide&Deep](./model_zoo/WideDeep) | [Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) :triangular_flag_on_post:**Google** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/WideDeep) | `torch`, `tf` | | 8 | ICDM'16 | [PNN](./model_zoo/PNN) | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/PNN) | `torch` | | 9 | KDD'16 | [DeepCrossing](./model_zoo/DeepCrossing) | [Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DeepCrossing) | `torch` | | 10 | NIPS'16 | [HOFM](./model_zoo/HOFM) | [Higher-Order Factorization Machines](https://papers.nips.cc/paper/6144-higher-order-factorization-machines.pdf) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/HOFM) | `torch` | | 11 | IJCAI'17 | [DeepFM](./model_zoo/DeepFM) | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/abs/1703.04247) :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DeepFM) | `torch`, `tf` | | 12 | SIGIR'17 | [NFM](./model_zoo/NFM) | [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/citation.cfm?id=3080777) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/NFM) | `torch` | | 13 | IJCAI'17 | [AFM](./model_zoo/AFM) | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/0435.pdf) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/AFM) | `torch` | | 14 | ADKDD'17 | [DCN](./model_zoo/DCN) | [Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) :triangular_flag_on_post:**Google** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DCN) | `torch`, `tf` | | 15 | WWW'18 | [FwFM](./model_zoo/FwFM) | [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) :triangular_flag_on_post:**Oath, TouchPal, LinkedIn, Alibaba** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FwFM) | `torch` | | 16 | KDD'18 | [xDeepFM](./model_zoo/xDeepFM) | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) :triangular_flag_on_post:**Microsoft** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/xDeepFM) | `torch` | | 17 | CIKM'19 | [FiGNN](./model_zoo/FiGNN) | [FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction](https://arxiv.org/abs/1910.05552) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FiGNN) | `torch` | | 18 | CIKM'19 | [AutoInt/AutoInt+](./model_zoo/AutoInt) | [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/AutoInt) | `torch` | | 19 | RecSys'19 | [FiBiNET](./model_zoo/FiBiNET) | [FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/abs/1905.09433) :triangular_flag_on_post:**Sina Weibo** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FiBiNET) | `torch` | | 20 | WWW'19 | [FGCNN](./model_zoo/FGCNN) | [Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction](https://arxiv.org/abs/1904.04447) :triangular_flag_on_post:**Huawei** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/FGCNN) | `torch` | | 21 | AAAI'19 | [HFM/HFM+](./model_zoo/HFM) | [Holographic Factorization Machines for Recommendation](https://ojs.aaai.org//index.php/AAAI/article/view/4448) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/HFM) | `torch` | | 22 | Arxiv'19 | [DLRM](./model_zoo/DLRM) | [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091) :triangular_flag_on_post:**Facebook** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/DLRM) | `torch` | | 23 | NeuralNetworks'20 | [ONN](./model_zoo/ONN) | [Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/ONN) | `torch`, `tf` | | 24 | AAAI'20 | [AFN/AFN+](./model_zoo/AFN) | [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://ojs.aaai.org/index.php/AAAI/article/view/5768) | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/AFN) | `torch` | | 25 | AAAI'20 | [LorentzFM](./model_zoo/LorentzFM) | [Learning Feature Interactions with Lorentzian Factorization](https://arxiv.org/abs/1911.09821) :triangular_flag_on_post:**eBay** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/LorentzFM) | `torch` | | 26 | WSDM'20 | [InterHAt](./model_zoo/InterHAt) | [Interpretable Click-through Rate Prediction through Hierarchical Attention](https://dl.acm.org/doi/10.1145/3336191.3371785) :triangular_flag_on_post:**NEC Labs, Google** | [:arrow_upper_right:](https://github.com/reczoo/BARS/tree/main/ranking/ctr/InterHAt) | `torch` | | 27 | DLP-KDD'20 | [FLEN](./model_zoo/FLEN) | [FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/abs/1911.04690) :triangular_flag_on_post:**Tencent**