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 | |:---:|:-----------------:|:----------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |:---------------------------------------------------------------------------------------------------------------:|:-------------:| |<tr><th colspan=6 align="center">:open_file_folder: Feature Interaction Models</th></tr>| | 1 | WWW'07 | LR | Predicting Clicks: Estimating the Click-Through Rate for New Ads :triangular_flag_on_post:Microsoft | :arrow_upper_right: | torch | | 2 | ICDM'10 | FM | Factorization Machines | :arrow_upper_right: | torch | | 3 | CIKM'13 | DSSM | Learning Deep Structured Semantic Models for Web Search using Clickthrough Data :triangular_flag_on_post:Microsoft | :arrow_upper_right: | torch | | 4 | CIKM'15 | CCPM | A Convolutional Click Prediction Model | :arrow_upper_right: | torch | | 5 | RecSys'16 | FFM | Field-aware Factorization Machines for CTR Prediction :triangular_flag_on_post:Criteo | :arrow_upper_right: | torch | | 6 | RecSys'16 | DNN | Deep Neural Networks for YouTube Recommendations :triangular_flag_on_post:Google | :arrow_upper_right: | torch, tf | | 7 | DLRS'16 | Wide&Deep | Wide & Deep Learning for Recommender Systems :triangular_flag_on_post:Google | :arrow_upper_right: | torch, tf | | 8 | ICDM'16 | PNN | Product-based Neural Networks for User Response Prediction | :arrow_upper_right: | torch | | 9 | KDD'16 | DeepCrossing | Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features :triangular_flag_on_post:Microsoft | :arrow_upper_right: | torch | | 10 | NIPS'16 | HOFM | Higher-Order Factorization Machines | :arrow_upper_right: | torch | | 11 | IJCAI'17 | DeepFM | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction :triangular_flag_on_post:Huawei | :arrow_upper_right: | torch, tf | | 12 | SIGIR'17 | NFM | Neural Factorization Machines for Sparse Predictive Analytics | :arrow_upper_right: | torch | | 13 | IJCAI'17 | AFM | Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | :arrow_upper_right: | torch | | 14 | ADKDD'17 | DCN | Deep & Cross Network for Ad Click Predictions :triangular_flag_on_post:Google | :arrow_upper_right: | torch, tf | | 15 | WWW'18 | FwFM | Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising :triangular_flag_on_post:Oath, TouchPal, LinkedIn, Alibaba | :arrow_upper_right: | torch | | 16 | KDD'18 | xDeepFM | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems :triangular_flag_on_post:Microsoft | :arrow_upper_right: | torch | | 17 | CIKM'19 | FiGNN | FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction | :arrow_upper_right: | torch | | 18 | CIKM'19 | AutoInt/AutoInt+ | AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | :arrow_upper_right: | torch | | 19 | RecSys'19 |