FuxiCTR

FuxiCTR

Click-through rate (CTR) prediction is a critical task for many 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 benefit both researchers and practitioners with the goal of open benchmarking for CTR prediction tasks.

If you find FuxiCTR useful in your research, please kindly cite the following papers:

Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. Open Benchmarking for Click-Through Rate Prediction. The 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021. [Bibtex]

Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022. [Bibtex]

Model Zoo

Publication	Model	Paper	Available
WWW'07	LR	Predicting Clicks: Estimating the Click-Through Rate for New Ads	✔️
ICDM'10	FM	Factorization Machines	✔️
CIKM'15	CCPM	A Convolutional Click Prediction Model	✔️
RecSys'16	FFM	Field-aware Factorization Machines for CTR Prediction	✔️
RecSys'16	YoutubeDNN	Deep Neural Networks for YouTube Recommendations	✔️
DLRS'16	Wide&Deep	Wide & Deep Learning for Recommender Systems	✔️
ICDM'16	IPNN	Product-based Neural Networks for User Response Prediction	✔️
KDD'16	DeepCrossing	Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features	✔️
NIPS'16	HOFM	Higher-Order Factorization Machines	✔️
IJCAI'17	DeepFM	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	✔️
SIGIR'17	NFM	Neural Factorization Machines for Sparse Predictive Analytics	✔️
IJCAI'17	AFM	Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks	✔️
ADKDD'17	DCN	Deep & Cross Network for Ad Click Predictions	✔️
WWW'18	FwFM	Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising	✔️
KDD'18	xDeepFM	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	✔️
KDD'18	DIN	Deep Interest Network for Click-Through Rate Prediction	✔️
CIKM'19	FiGNN	FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction	✔️
CIKM'19	AutoInt/AutoInt+	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	✔️
RecSys'19	FiBiNET	FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction	✔️
WWW'19	FGCNN	Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction	✔️
AAAI'19	HFM/HFM+	Holographic Factorization Machines for Recommendation	✔️
AAAI'19	DIEN	Deep Interest Evolution Network for Click-Through Rate Prediction	✔️
DLP-KDD'19	BST	Behavior Sequence Transformer for E-commerce Recommendation in Alibaba	✔️
Neural Networks'20	ONN	Operation-aware Neural Networks for User Response Prediction	✔️
AAAI'20	AFN/AFN+	Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions	✔️
AAAI'20	LorentzFM	Learning Feature Interactions with Lorentzian Factorization	✔️
WSDM'20	InterHAt	Interpretable Click-through Rate Prediction through Hierarchical Attention	✔️
DLP-KDD'20	FLEN	FLEN: Leveraging Field for Scalable CTR Prediction	✔️
WWW'21	FmFM	FM^2: Field-matrixed Factorization Machines for Recommender Systems	✔️
WWW'21	DCN-V2	DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	✔️
CIKM'21	DESTINE	Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction	✔️
CIKM'21	EDCN	Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models	✔️
DLP-KDD'21	MaskNet	MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask	✔️
SIGIR'21	SAM	Looking at CTR Prediction Again: Is Attention All You Need?	✔️
KDD'21	AOANet	Architecture and Operation Adaptive Network for Online Recommendations	✔️
IJCAI'21	UNBERT	UNBERT: User-News Matching BERT for News Recommendation	✔️
CIKM'22	SDIM	Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction	✔️

Dependency

FuxiCTR has the following dependent requirements.

• pytorch 1.10+
• python 3.6+
• pyyaml 5.1+
• scikit-learn
• pandas
• numpy
• h5py
• tqdm

Get Started

One can easily run each model in the model zoo following the commands below, which is a demo for running DCN. In addition, users can modify the dataset config and model config files to run on their own datasets or on new hyper-parameters.

cd model_zoo/DCN/DCN_torch
python run_expid --expid DCN_test --gpu 0

Join Us

We have open positions for internships and full-time jobs. If you are interested in research and practice in recommender systems, please send your CV to jamie.zhu@huawei.com.