The source code of MacGNN, The Web Conference 2024.
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Updated
May 28, 2024 - Python
The source code of MacGNN, The Web Conference 2024.
推荐算法实战(Recommend algorithm)
A curated list of papers on click-through-rate (CTR) prediction.
Implementation of algorithms for click through rate predictions utilising sparsity.
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
The Most Complete PyTorch Implementation of "Deep Interest Network for Click-Through Rate Prediction"
This is an official implementation of feature interaction for BaGFN
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).
StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies.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.
LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.
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.
Code for the IJCAI'19 paper "Deep Session 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
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.
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.
Dataset and code for “Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction”
Training pipeline using TFRecord files
Must-read Papers for Recommender Systems (RS)
An introduction of a simple approach for CTR Anomaly Detection
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