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.

👉 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

  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
GitHub Repository
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
[ICCV 2021 Oral] Just Ask: Learning to Answer Questions from Millions of Narrated Videos

Just Ask: Learning to Answer Questions from Millions of Narrated Videos Webpage • Demo • Paper This repository provides the code for our paper, includ

Antoine Yang 87 Jan 05, 2023
Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges for the practitioner

Sparse network learning with snlpy Very large and sparse networks appear often in the wild and present unique algorithmic opportunities and challenges

Andrew Stolman 1 Apr 30, 2021
TransVTSpotter: End-to-end Video Text Spotter with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 66 Dec 26, 2022
C3D is a modified version of BVLC caffe to support 3D ConvNets.

C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co

Meta Archive 1.1k Nov 14, 2022
Where-Got-Time - An NUS timetable generator which uses a genetic algorithm to optimise timetables to suit the needs of NUS students

Where Got Time(table)? A timetable optimsier which uses an evolutionary algorith

Nicholas Lee 3 Jan 09, 2022
D2Go is a toolkit for efficient deep learning

D2Go D2Go is a production ready software system from FacebookResearch, which supports end-to-end model training and deployment for mobile platforms. W

Facebook Research 744 Jan 04, 2023
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction

DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction This is the implementation of DeepSTD in

5 Sep 26, 2022
The Noise Contrastive Estimation for softmax output written in Pytorch

An NCE implementation in pytorch About NCE Noise Contrastive Estimation (NCE) is an approximation method that is used to work around the huge computat

Kaiyu Shi 287 Nov 25, 2022
Training DiffWave using variational method from Variational Diffusion Models.

Variational DiffWave Training DiffWave using variational method from Variational Diffusion Models. Quick Start python train_distributed.py discrete_10

Chin-Yun Yu 26 Dec 13, 2022
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising (CVPR 2020 Oral & TPAMI 2021)

ELD The implementation of CVPR 2020 (Oral) paper "A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising" and its journal (TPAMI) v

Kaixuan Wei 359 Jan 01, 2023
Only works with the dashboard version / branch of jesse

Jesse optuna Only works with the dashboard version / branch of jesse. The config.yml should be self-explainatory. Installation # install from git pip

Markus K. 8 Dec 04, 2022
Pytorch-diffusion - A basic PyTorch implementation of 'Denoising Diffusion Probabilistic Models'

PyTorch implementation of 'Denoising Diffusion Probabilistic Models' This reposi

Arthur Juliani 76 Jan 07, 2023
Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library.

SymEngine Python Wrappers Python wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library. Installation Pip See License section

136 Dec 28, 2022
Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

MediumVC MediumVC is an utterance-level method towards any-to-any VC. Before that, we propose SingleVC to perform A2O tasks(Xi → Ŷi) , Xi means utter

谷下雨 47 Dec 25, 2022
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022
The Multi-Mission Maximum Likelihood framework (3ML)

PyPi Conda The Multi-Mission Maximum Likelihood framework (3ML) A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.

The Multi-Mission Maximum Likelihood (3ML) 62 Dec 30, 2022
A stable algorithm for GAN training

DRAGAN (Deep Regret Analytic Generative Adversarial Networks) Link to our paper - https://arxiv.org/abs/1705.07215 Pytorch implementation (thanks!) -

195 Oct 10, 2022
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

32 Dec 26, 2022