Improving Real-Time Bidding in Online Advertising using Markov Decision Processes and Machine Learning Techniques
Parikshit Sharma

Parikshit Sharma, Department of Mathematics, Birla Institute of Technology and Science, Pilani (Rajasthan), India.

Manuscript received on 16 June 2023 | Revised Manuscript received on 04 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 1-8 | Volume-10 Issue-7, July 2023 | Retrieval Number: 100.1/ijaent.F42310812623 | DOI: 10.35940/ijaent.F4231.0710723
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Abstract: Real-time bidding has emerged as an effective online advertising technique. With real-time bidding, advertisers can position ads per impression, enabling them to optimise ad campaigns by targeting specific audiences in real-time. This paper proposes a novel method for real-time bidding that combines deep learning and reinforcement learning techniques to enhance the efficiency and precision of the bidding process. In particular, the proposed method employs a deep neural network to predict auction details and market prices and a reinforcement learning algorithm to determine the optimal bid price. The model is trained using historical data from the iPin You dataset and compared to cutting-edge real-time bidding algorithms. The outcomes demonstrate that the proposed method is preferable regarding cost-effectiveness and precision. In addition, the study investigates the influence of various model parameters on the performance of the proposed algorithm. It offers insights into the efficacy of the combined deep learning and reinforcement learning approach for real-time bidding. This study contributes to advancing techniques and offers a promising direction for future research. 
Keywords: Real-time bidding, Display advertising, Reinforcement learning, Markov decision process, Deep landscape forecasting
Scope of the Article: Deep learning