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.

翻译：实时竞价已成为一种高效的在线广告技术。通过实时竞价，广告主可针对每次展示进行广告位出价，从而能够通过实时定位特定受众来优化广告活动。本文提出了一种结合深度学习和强化学习技术的新型实时竞价方法，旨在提升竞价过程的效率与精确度。具体而言，所提方法采用深度神经网络预测拍卖细节与市场价格，并利用强化学习算法确定最优出价。模型基于iPinYou数据集的历史数据进行训练，并与当前最先进的实时竞价算法进行了对比。结果表明，所提方法在成本效益和精确度方面均更具优势。此外，本研究探讨了不同模型参数对所提算法性能的影响，并深入分析了深度学习和强化学习联合方法在实时竞价中的有效性。本研究推动了相关技术的发展，并为未来研究提供了有前景的方向。