Analysis and Implementation of Web Advertisement Optimization Using UCB Reinforcement Learning

Abstract

Web advertisements are vital in digital marketing for connecting businesses with customers; yet optimizing their placement faces challenges from evolving user preferences. This project explores leveraging the Upper Confidence Bound (UCB) algorithm, a reinforcement learning technique, to automate ad placement strategies based on real-time feedback. Through analysis and implementation, this study investigates UCB's potential for web advertisement optimization. It examines existing methods, delves into reinforcement learning theory, and develops a simulation environment reflecting real-world dynamics. Experimental results comparing the UCB approach against baselines demonstrate its efficacy in improving ad placement and enhancing online advertising campaigns.