Ad Optimization Via Machine Learning: A Focus on Upper Confidence Bound and Thompson Sampling Algorithms

Abstract

The objective of this project is to improve the effectiveness and efficiency of advertising on various platforms by utilizing advanced algorithms, namely the Upper Confidence Bound and Thompson Sampling Algorithm. The project aims to find a balance between exploring new advertising strategies and exploiting proven high-performing approaches. By implementing these bandit algorithms, the project aims to dynamically optimize ad placements, formats, and targeting to maximize user engagement and ad revenue. The methodology involves an iterative process of data collection, analysis, and adaptation. The initial phases include defining project objectives, understanding the target audience, and reviewing the current ad strategy. The Upper Confidence Bound algorithm enables intelligent decision-making by assigning confidence bounds to different ad strategies, allowing for efficient exploration and exploitation. On the other hand, the Thompson Sampling algorithm, rooted in Bayesian principles, dynamically adapts based on observed outcomes, striking a balance between exploration and exploitation through probabilistic reasoning. In summary, this Ads Optimization Project utilizes the power of the Upper Confidence Bound and Thompson Sampling algorithms to create a data-driven, adaptive, and user-centric approach to advertising. The ultimate goal is to achieve optimal user engagement and ad revenue.