click-through-rate

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

Tensorflow implementation of DeepFM for CTR prediction.

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

推荐算法实战(Recommend algorithm)

Must-read Papers for Recommender Systems (RS)

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.

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

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

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

The source code of MacGNN, The Web Conference 2024.

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

Click-Through Rate Estimation for Rare Events in Online Advertising

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

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.

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

This is an official implementation of feature interaction for BaGFN

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

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

Training pipeline using TFRecord files

StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies.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.