CS7CS4- Machine Learning- Recommendation Algorithm- Click Prediction- Kaggle Competition

Implementation of algorithms for click through rate predictions utilising sparsity.

Recommendation system implementation

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.

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.

An introduction of a simple approach for CTR Anomaly Detection

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

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

StrikePrick is your one-stop destination for exposing and overturning ineffective, outdated email marketing strategies.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

Training pipeline using TFRecord files

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

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.

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

Click-Through Rate Estimation for Rare Events in Online Advertising

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

The source code of MacGNN, The Web Conference 2024.

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