A configurable, tunable, and reproducible library for CTR prediction

Related tags

Deep Learningpytorchrecommender-systemsctr-predictionctrcvr
Overview

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.

πŸ‘‰ 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 βœ”οΈ
ICDM'10 FM Factorization Machines βœ”οΈ
CIKM'15 CCPM A Convolutional Click Prediction Model βœ”οΈ
RecSys'16 FFM Field-aware Factorization Machines for CTR Prediction βœ”οΈ
RecSys'16 YoutubeDNN Deep Neural Networks for YouTube Recommendations βœ”οΈ
DLRS'16 Wide&Deep Wide & Deep Learning for Recommender Systems βœ”οΈ
ICDM'16 IPNN Product-based Neural Networks for User Response Prediction βœ”οΈ
KDD'16 DeepCross Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features βœ”οΈ
NIPS'16 HOFM Higher-Order Factorization Machines βœ”οΈ
IJCAI'17 DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction βœ”οΈ
SIGIR'17 NFM Neural Factorization Machines for Sparse Predictive Analytics βœ”οΈ
IJCAI'17 AFM Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks βœ”οΈ
ADKDD'17 DCN Deep & Cross Network for Ad Click Predictions βœ”οΈ
WWW'18 FwFM Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising βœ”οΈ
KDD'18 xDeepFM xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems βœ”οΈ
KDD'18 DIN Deep Interest Network for Click-Through Rate Prediction βœ”οΈ
CIKM'19 FiGNN FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction βœ”οΈ
CIKM'19 AutoInt/AutoInt+ AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks βœ”οΈ
RecSys'19 FiBiNET FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction βœ”οΈ
WWW'19 FGCNN Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction βœ”οΈ
AAAI'19 HFM/HFM+ Holographic Factorization Machines for Recommendation βœ”οΈ
NeuralNetworks'20 ONN Operation-aware Neural Networks for User Response Prediction βœ”οΈ
AAAI'20 AFN/AFN+ Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions βœ”οΈ
AAAI'20 LorentzFM Learning Feature Interactions with Lorentzian Factorization βœ”οΈ
WSDM'20 InterHAt Interpretable Click-through Rate Prediction through Hierarchical Attention βœ”οΈ
DLP-KDD'20 FLEN FLEN: Leveraging Field for Scalable CTR Prediction βœ”οΈ
CIKM'20 DeepIM Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions βœ”οΈ
WWW'21 FmFM FM^2: Field-matrixed Factorization Machines for Recommender Systems βœ”οΈ
WWW'21 DCN-V2 DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems βœ”οΈ

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. Preprocess raw csv data to h5 data

  4. Run a model with h5 data as input

  5. How to make configurations?

  6. Tune the model hyper-parameters via grid search

  7. Run a model with sequence features

  8. Run a model with pretrained embeddings

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].

Owner
XUEPAI
XUEPAI
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