Optimizing Real-Time Bidding Strategies: An Experimental Analysis of Reinforcement Learning and Machine Learning Techniques

Abstract

This research provides an experimental analysis and comparison of several RTB (real-time bidding) methods. These experiments are based on practical validation through the utilization of real-world datasets, with the primary attention being placed on the dynamic and complex nature of the RTB ecosystem. Simulations are run on various real-world advertising campaigns as part of the experimentation. These simulations take into account different campaign budgets, bid request dynamics, and user interaction pattern variations. The efficiency of each algorithm is evaluated based on performance indicators such as click-through rates (CTRs), conversion rates (CR), return on investment (ROI), win rates, cost per mille (CPM), and effective cost per click (E-CPC). The results provide useful insights into the advantages and disadvantages of each technique used, and the experimental analysis indicates the Constrained Markov Decision Process (CMDP)-based model as a promising and superior technique for RTB optimization. It provides valuable information on applications of reinforcement learning in the dynamic RTB ecosystem. Hence, via these comparisons, this paper aims to contribute to the advancement of RTB methodologies and proposes a viable route for future research.