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
NLG evaluation via Statistical Measures of Similarity: BaryScore, DepthScore, InfoLM

NLG evaluation via Statistical Measures of Similarity: BaryScore, DepthScore, InfoLM Automatic Evaluation Metric described in the papers BaryScore (EM

Pierre Colombo 28 Dec 28, 2022
Face Identity Disentanglement via Latent Space Mapping [SIGGRAPH ASIA 2020]

Face Identity Disentanglement via Latent Space Mapping Description Official Implementation of the paper Face Identity Disentanglement via Latent Space

150 Dec 07, 2022
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want

Funny_muscle_enhancer :) 1.Discription: This is just a funny project that we want to see AutoEncoder (AE) can actually work on the some features. We w

Jing-Yao Chen (Jacob) 8 Oct 01, 2022
SIMULEVAL A General Evaluation Toolkit for Simultaneous Translation

SimulEval SimulEval is a general evaluation framework for simultaneous translation on text and speech. Requirement python = 3.7.0 Installation git cl

Facebook Research 48 Dec 28, 2022
Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Transfer learning approach to bicycle sharing systems station location planning using OpenStreetMap Companion repository to the paper accepted at the

Politechnika Wrocławska - repozytorium dla informatyków 4 Oct 24, 2022
How to Learn a Domain Adaptive Event Simulator? ACM MM, 2021

LETGAN How to Learn a Domain Adaptive Event Simulator? ACM MM 2021 Running Environment: pytorch=1.4, 1 NVIDIA-1080TI. More details can be found in pap

CVTEAM 4 Sep 20, 2022
Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash through feeding it pictures or videos.

Trash-Sorter-Extraordinaire Trash Sorter Extraordinaire is a software which efficiently detects the different types of waste in a pile of random trash

Rameen Mahmood 1 Nov 07, 2021
Using a Seq2Seq RNN architecture via TensorFlow to predict future Bitcoin prices

Recurrent Bitcoin Network A Data Science Thesis Project About This repository contains the source code for implementing Bitcoin price prediciton using

Frizu 6 Sep 08, 2022
Code for the paper "M2m: Imbalanced Classification via Major-to-minor Translation" (CVPR 2020)

M2m: Imbalanced Classification via Major-to-minor Translation This repository contains code for the paper "M2m: Imbalanced Classification via Major-to

79 Oct 13, 2022
The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation

Maxim Zaika 1 Nov 17, 2021
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

MPLP: Metapath-Based Label Propagation for Heterogenous Graphs Results on MAG240M Here, we demonstrate the following performance on the MAG240M datase

Qiuying Peng 10 Jun 28, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

Aquarius Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions NOTE: We are currently going through the open-source process requir

Zhiyuan YAO 0 Jun 02, 2022
U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras This tutorial shows how to use Keras library to build deep ne

Yihui He 401 Nov 21, 2022
CONditionals for Ordinal Regression and classification in tensorflow

Condor Ordinal regression in Tensorflow Keras Tensorflow Keras implementation of CONDOR Ordinal Regression (aka ordinal classification) by Garrett Jen

9 Jul 31, 2022
Repo for CVPR2021 paper "QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information"

QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information by Masato Tamura, Hiroki Ohashi, and Tomoaki Yosh

105 Dec 23, 2022
PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch NeRF and pixelNeRF NeRF: Tiny NeRF: pixelNeRF: This repository contains minimal PyTorch implementations of the NeRF model described in "NeRF:

Michael A. Alcorn 178 Dec 20, 2022
SIEM Logstash parsing for more than hundred technologies

LogIndexer Pipeline Logstash Parsing Configurations for Elastisearch SIEM and OpenDistro for Elasticsearch SIEM Why this project exists The overhead o

146 Dec 29, 2022
(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"

(CVPR 2022) TokenCut Pytorch implementation of Tokencut: Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut Yangtao W

YANGTAO WANG 200 Jan 02, 2023