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

TL;DR

This paper introduces a novel real-time bidding method that combines deep learning and reinforcement learning to improve ad auction efficiency and targeting precision, trained on historical data and outperforming existing algorithms.

Contribution

It presents a new integrated deep learning and reinforcement learning approach for real-time bidding, demonstrating enhanced cost-effectiveness and accuracy over current methods.

Findings

The proposed method outperforms existing algorithms in cost and precision.

Model parameter variations significantly impact performance.

Deep learning combined with reinforcement learning is effective for real-time bidding.

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 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.