FuxiCTR

This repo is the community dev version of the official release at huawei-noah/benchmark/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 key features in configurability, tunability, and reproducibility. It also supports the building of the BARS-CTR-Prediction benchmark, which aims for open benchmarking for CTR prediction.

👉 If you find our code or benchmarks helpful in your research, please kindly cite the following paper.

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.

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	✔️
NeuralNetworks'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	✔️
CIKM'20	DeepIM	Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions	✔️
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	✔️

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

Code Structure

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

Open Benchmarking

If you are looking for the benchmarking settings and results on the state-of-the-art CTR prediction models, please refer to the BARS-CTR-Prediction benchmark. By clicking on the "SOTA Results", you will find the benchmarking results along with the corresponding reproducing steps.

Discussion

Welcome to join our WeChat group for any questions and discussions.

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].

A configurable, tunable, and reproducible library for CTR prediction

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Overview

FuxiCTR

Model List

Installation

Get Started

Code Structure

Open Benchmarking

Discussion

Join Us

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XUEPAI

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