Optimized Click-Through Rate Prediction Using Multi-Armed Bandit Algorithms

Abstract

This project applies Multi-Armed Bandit algorithms to enhance online ad click-through rates, specifically utilizing the epsilon-greedy algorithm for balancing ad strategy exploration with the exploitation of high-performing ads.

Keywords

Click-Through Rate, CTR Prediction, Multi-Armed Bandit, Epsilon-Greedy Algorithm, Digital Advertising

Project Structure

Section	Description
Data	Dataset overview and source.
Analysis	Analysis of ad performance data.
Implementation	Description of the epsilon-greedy algorithm implementation.
Results	Summary of findings and optimization results.

Usage

This project consists of an R Markdown analysis (project.Rmd), and a dataset (my_data.csv). To replicate the study or explore the data with our analyses, follow these steps:

1. Prepare Your Environment:

   • Ensure you have R plus RStudio installed.
   • Install required R packages with install.packages(c("package1", "package2", ...)).

2. Download the Project Files:

   • Clone this repository or download the files directly.

3. Run the Analysis:

   • Open project.Rmd in RStudio to view the primary analysis.
   • To execute the code and generate the report, click on "Knit" in RStudio.

4. Explore the Dataset:

   • Load my_data.csv into R using read.csv('path/to/my_data.csv') to explore the dataset independently.

Acknowledgements

This project utilizes the "Click-Through Rate Prediction" dataset, available on Kaggle. The dataset is instrumental in evaluating ad performance through click-through rate metrics, crucial for optimizing ad prediction systems in online advertising.

License

The usage of the dataset follows the terms and conditions provided by Kaggle and in particular, the data holder, Avazu. For more detailed information, please refer to the: https://www.kaggle.com/c/avazu-ctr-prediction.