click-through-rate

There are 30 repositories under click-through-rate topic.

  • DeepCTR

    shenweichen/DeepCTR

    Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

    Language:Python7.5k1783692.2k

  • ChenglongChen/tensorflow-DeepFM

    Tensorflow implementation of DeepFM for CTR prediction.

    Language:Python2k6780807

  • shenweichen/DSIN

    Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

    Language:Python4321423131

  • hegongshan/Recommender-Systems-Paper

    Must-read Papers for Recommender Systems (RS)

    1461018

  • HuichuanLI/Recommand-Algorithme

    推荐算法实战(Recommend algorithm)

    Language:Jupyter Notebook1193025

  • Hirosora/LightCTR

    LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.

    Language:Python1026427

  • p768lwy3/torecsys

    ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.

    Language:Python1015317

  • qian135/ctr_model_zoo

    some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network

    Language:Jupyter Notebook703114

  • fanoping/DIN-pytorch

    PyTorch Implementation of Deep Interest Network for Click-Through Rate Prediction

    Language:Python661219

  • YuanchenBei/MacGNN

    The source code of MacGNN, The Web Conference 2024.

    Language:Python46235

  • 1146976048qq/MIAN-CTR

    Dataset and code for “Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction”

    Language:Python16114

  • YuyuZha0/bayes-smoothing

    Click-Through Rate Estimation for Rare Events in Online Advertising

    Language:Java12237

  • farrellwahyudi/Predicting-Ad-Clicks-Classification-by-Using-Machine-Learning

    In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.

    Language:Jupyter Notebook8101

  • YuanchenBei/Awesome-Click-Through-Rate-Prediction

    A curated list of papers on click-through-rate (CTR) prediction.

    8200

  • alicogintel/DSIN

    Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

    Language:Python7205

  • YuanchenBei/NRCGI

    The source code of NRCGI (Non-Recursive Cluster-Scale Graph Interacted Model for Click-Through Rate Prediction, CIKM2023).

    Language:Python5100

  • VladOnMyOwn/ctr-poisson-bootstrap

    Here I demonstrate the performance difference between the Poisson and the classic bootstrap by estimating the confidence interval for the difference of CTRs of the two user groups

    Language:Jupyter Notebook4102

  • vmipshu/BaGFN

    This is an official implementation of feature interaction for BaGFN

    Language:Python4100

  • rdolor/train-tfrecords

    Training pipeline using TFRecord files

    Language:Python3200

  • yeyingdege/ctr-din-pytorch

    The Most Complete PyTorch Implementation of "Deep Interest Network for Click-Through Rate Prediction"

    Language:Python3100

  • shadowaxe99/strikeprick

    StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies. This repository offers a data-driven, humor-infused critique of commonly touted advice, using verified statistics to debunk myths and set the record straight. Designed for e-commerce brands and marketers.

    Language:Python2101

  • Abuton/Intermediate-DS-Projects

    I went on a 5 days sprint of completing some of my previously started projects and i hope to have 4 project deployed at the end of the 5th day.

    Language:Jupyter Notebook1200

  • imsheridan/xDeepRank

    An eXtensible Package of Deep Learning based Ranking Models for Large-scale Industrial Recommender System with Tensorflow

    Language:Python1100

  • MingalievDinar/adverity

    An introduction of a simple approach for CTR Anomaly Detection

    Language:Jupyter Notebook1200

  • ahmadara/CTR-Predeiction

    Language:Jupyter Notebook00

  • faizns/Predict-Clicked-Ads-Customer-Classification

    This repository contains a machine learning model for predicting customer click-through rate on ads. By analyzing user demographics and browsing behavior, the model aims to identify potential customers with a higher likelihood of clicking on ads.

    Language:Jupyter Notebook0100

  • GNOEYHEAT/CTR_stacking

    웹 광고 클릭률 예측 AI 경진대회, DACON (2024.05.07 ~ 2024.06.03)

    Language:Python0101

  • ksolarski/effCTR

    Implementation of algorithms for click through rate predictions utilising sparsity.

    Language:Jupyter Notebook0100

  • stmunees/ML-Kaggle-02

    CS7CS4- Machine Learning- Recommendation Algorithm- Click Prediction- Kaggle Competition

    Language:Jupyter Notebook0100

  • pingsutw/Recommendation-system

    Recommendation system implementation

    Language:Python20