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
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022
Encode and decode text application

Text Encoder and Decoder Encode and decode text in many ways using this application! Encode in: ASCII85 Base85 Base64 Base32 Base16 Url MD5 Hash SHA-1

Alice 1 Feb 12, 2022
Vector Quantized Diffusion Model for Text-to-Image Synthesis

Vector Quantized Diffusion Model for Text-to-Image Synthesis Due to company policy, I have to set microsoft/VQ-Diffusion to private for now, so I prov

Shuyang Gu 294 Jan 05, 2023
Datasets for new state-of-the-art challenge in disentanglement learning

High resolution disentanglement datasets This repository contains the Falcor3D and Isaac3D datasets, which present a state-of-the-art challenge for co

NVIDIA Research Projects 37 May 26, 2022
Detecting drunk people through thermal images using Deep Learning (CNN)

Drunk Detection CNN Detecting drunk people through thermal images using Deep Learning (CNN) Dataset We used thermal images provided by Electronics Lab

Giacomo Ferretti 3 Oct 27, 2022
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

Self-Supervised Vision Transformers with DINO PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supe

Facebook Research 4.2k Jan 03, 2023
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Shichen Liu 690 Nov 30, 2022
Cross-platform CLI tool to generate your Github profile's stats and summary.

ghs Cross-platform CLI tool to generate your Github profile's stats and summary. Preview Hop on to examples for other usecases. Jump to: Installation

HackerRank 134 Dec 20, 2022
[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

Discriminative Region-based Multi-Label Zero-Shot Learning (ICCV 2021) [arXiv][Project page coming soon] Sanath Narayan*, Akshita Gupta*, Salman Kh

Akshita Gupta 54 Nov 21, 2022
Discover hidden deepweb pages

DeepWeb Scapper Att: Demo version An simple script to scrappe deepweb to find pages. Will return if any of those exists and will save on a file. You s

Héber Júlio 77 Oct 02, 2022
Pytorch code for "Text-Independent Speaker Verification Using 3D Convolutional Neural Networks".

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

Amirsina Torfi 114 Dec 18, 2022
ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

This is the project page for the paper: ISTR: End-to-End Instance Segmentation via Transformers, Jie Hu, Liujuan Cao, Yao Lu, ShengChuan Zhang, Yan Wa

Jie Hu 182 Dec 19, 2022
Few-Shot Object Detection via Association and DIscrimination

Few-Shot Object Detection via Association and DIscrimination Code release of our NeurIPS 2021 paper: Few-Shot Object Detection via Association and DIs

Cao Yuhang 49 Dec 18, 2022
A GridMixup augmentation, inspired by GridMask and CutMix

GridMixup A GridMixup augmentation, inspired by GridMask and CutMix Easy install pip install git+https://github.com/IlyaDobrynin/GridMixup.git Overvie

IlyaDo 42 Dec 28, 2022
TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video

TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video Timely handgun detection is a cr

Mario Duran-Vega 18 Dec 26, 2022
Human Detection - Pedestrian Detection using OpenCV Python

Pedestrian Detection using OpenCV Python Follow us on Instagram for Machine Lear

Hrishikesh Dutta 1 Jan 23, 2022
Really awesome semantic segmentation

really-awesome-semantic-segmentation A list of all papers on Semantic Segmentation and the datasets they use. This site is maintained by Holger Caesar

Holger Caesar 400 Nov 28, 2022
Code for "R-GCN: The R Could Stand for Random"

RR-GCN: Random Relational Graph Convolutional Networks PyTorch Geometric code for the paper "R-GCN: The R Could Stand for Random" RR-GCN is an extensi

PreDiCT.IDLab 31 Sep 07, 2022
PyTorch implementation of UNet++ (Nested U-Net).

PyTorch implementation of UNet++ (Nested U-Net) This repository contains code for a image segmentation model based on UNet++: A Nested U-Net Architect

4ui_iurz1 642 Jan 04, 2023
Automatic library of congress classification, using word embeddings from book titles and synopses.

Automatic Library of Congress Classification The Library of Congress Classification (LCC) is a comprehensive classification system that was first deve

Ahmad Pourihosseini 3 Oct 01, 2022