A configurable, tunable, and reproducible library for CTR prediction

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License: Apache Software License (Apache-2.0 License)

Author: zhujiem

Tags ctr prediction, recommender systems, ctr, cvr, pytorch

Requires: Python ==3.6.*

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Project description

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.

:bell: 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 Benchmark
WWW'07 LR Predicting Clicks: Estimating the Click-Through Rate for New Ads :arrow_upper_right:
ICDM'10 FM Factorization Machines :arrow_upper_right:
CIKM'15 CCPM A Convolutional Click Prediction Model :arrow_upper_right:
RecSys'16 FFM Field-aware Factorization Machines for CTR Prediction :arrow_upper_right:
RecSys'16 YoutubeDNN Deep Neural Networks for YouTube Recommendations :arrow_upper_right:
DLRS'16 Wide&Deep Wide & Deep Learning for Recommender Systems :arrow_upper_right:
ICDM'16 IPNN Product-based Neural Networks for User Response Prediction :arrow_upper_right:
KDD'16 DeepCross Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features :arrow_upper_right:
NIPS'16 HOFM Higher-Order Factorization Machines :arrow_upper_right:
IJCAI'17 DeepFM DeepFM: A Factorization-Machine based Neural Network for CTR Prediction :arrow_upper_right:
SIGIR'17 NFM Neural Factorization Machines for Sparse Predictive Analytics :arrow_upper_right:
IJCAI'17 AFM Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks :arrow_upper_right:
ADKDD'17 DCN Deep & Cross Network for Ad Click Predictions :arrow_upper_right:
WWW'18 FwFM Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising :arrow_upper_right:
KDD'18 xDeepFM xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems :arrow_upper_right:
KDD'18 DIN Deep Interest Network for Click-Through Rate Prediction :arrow_upper_right:
CIKM'19 FiGNN FiGNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction :arrow_upper_right:
CIKM'19 AutoInt/AutoInt+ AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks :arrow_upper_right:
RecSys'19 FiBiNET FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction :arrow_upper_right:
WWW'19 FGCNN Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction :arrow_upper_right:
AAAI'19 HFM/HFM+ Holographic Factorization Machines for Recommendation :arrow_upper_right:
NeuralNetworks'20 ONN Operation-aware Neural Networks for User Response Prediction :arrow_upper_right:
AAAI'20 AFN/AFN+ Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions :arrow_upper_right:
AAAI'20 LorentzFM Learning Feature Interactions with Lorentzian Factorization :arrow_upper_right:
WSDM'20 InterHAt Interpretable Click-through Rate Prediction through Hierarchical Attention :arrow_upper_right:
DLP-KDD'20 FLEN FLEN: Leveraging Field for Scalable CTR Prediction :arrow_upper_right:
CIKM'20 DeepIM Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions :arrow_upper_right:
WWW'21 FmFM FM^2: Field-matrixed Factorization Machines for Recommender Systems :arrow_upper_right:
WWW'21 DCN-V2 DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems :arrow_upper_right:

:bell: If you are looking for the benchmarking settings and results of the above CTR prediction models, please click on the benchmark hyperlinks in the table. You could also refer to the BARS-CTR-Prediction website for more details.

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

User Guide

  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

Developer Guide

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

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 jamie.zhu@huawei.com.

Project details

Verified details

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Project links
GitHub Statistics

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Meta

License: Apache Software License (Apache-2.0 License)

Author: zhujiem

Tags ctr prediction, recommender systems, ctr, cvr, pytorch

Requires: Python ==3.6.*

Classifiers

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2.3.0

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Reason this release was yanked:

Deprecated due to bug fixing. Please install fuxictr==2.2.3 instead.

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