FuxiCTR

FuxiCTR

Click-through rate (CTR) prediction is an 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 stunning features in configurability, tunability, and reproducibility.

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, in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.
• Jieming Zhu, Kelong Mao, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Zhicheng Dou, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems, in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022.

Model List

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	DeepCross	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	✔️
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	✔️
IJCAI'21	UNBERT	UNBERT: User-News Matching BERT for News Recommendation	✔️

Installation

Please follow the guide for installation. In particular, FuxiCTR has the following dependent requirements.

• python 3.6
• pytorch v1.0/v1.1
• pyyaml >=5.1
• scikit-learn
• pandas
• numpy
• h5py
• tqdm

Get Started

1. Run the demo to understand the overall workflow

2. Run a model with dataset and model config files

3. How to make configurations?

4. Tune the model hyper-parameters via grid search

Code Structure

Check an overview of code structure for more details on API design.

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 [email protected].