Haider, UmairYıldız, BeytullahYildiz, BeytullahSoftware Engineering2024-09-102024-09-1020230979835036151397983503723042769-567010.1109/CSCI62032.2023.000172-s2.0-85199984713https://doi.org/10.1109/CSCI62032.2023.00017YILDIZ, Beytullah/0000-0001-7664-5145Efficiently predicting Click-through Rate (CTR) is crucial for the success of online advertising. Traditional methods often struggle to adapt to the dynamic nature of user preferences and the evolving relevance of advertisements. In this study, we propose a novel Reinforcement Learning (RL) approach for CTR prediction, leveraging OpenAI Gym and the Thompson Sampling algorithm. Our approach dynamically estimates CTR, cleverly adapting to the ever-changing landscape of user preferences and advertisement relevance. Results showcase the exceptional performance of Thompson Sampling in CTR prediction, sur-passing other RL methods with a remarkable 10% higher confidence level. This emphasizes the significant potential of our RL approach in optimizing the selection of online advertisements.eninfo:eu-repo/semantics/closedAccessReinforcement LearningOpenAl Gym EnvironmentThompson SamplingClick-through rateOnline AdvertisingConference ObjectN/AN/A6470WOS:001283930300019