Training pipeline using TFRecord files

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

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

Recommendation system implementation

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

This is an official implementation of feature interaction for BaGFN

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

StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies.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

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

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

The source code of MacGNN, The Web Conference 2024.

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.

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

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

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

Tensorflow implementation of DeepFM for CTR prediction.

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