A Novel Use of Reinforcement Learning for Elevated Click-Through Rate in Online Advertising
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee Computer Soc
Abstract
Efficiently 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.
Description
YILDIZ, Beytullah/0000-0001-7664-5145
Keywords
Reinforcement Learning, OpenAl Gym Environment, Thompson Sampling, Click-through rate, Online Advertising
Turkish CoHE Thesis Center URL
Citation
0
WoS Q
N/A
Scopus Q
N/A
Source
International Conference on Computational Science and Computational Intelligence (CSCI) -- DEC 13-15, 2023 -- Las Vegas, NV
Volume
Issue
Start Page
64
End Page
70