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