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