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"

Must-read Papers for Recommender Systems (RS)

推荐算法实战(Recommend algorithm)

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

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.

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

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

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

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.

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

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

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

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

This is an official implementation of feature interaction for BaGFN

Training pipeline using TFRecord files

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