论文标题
展示广告中的实时投标策略：实证分析
Real-time Bidding Strategy in Display Advertising: An Empirical Analysis
论文作者
论文摘要
随着越来越多的广告印象通过实时招标系统出售，可以帮助广告商确定投标价格的竞标策略正在受到越来越多的关注。本文首先描述了在实时招标显示广告中优化单个广告商的招标策略的问题和挑战。然后，引入了几种代表性的招标策略，尤其是基于强化学习的招标策略的研究进展和挑战。此外，我们定量评估了ipinyou数据集上几种代表性招标策略的性能。具体而言，我们研究了国家，行动和奖励功能对基于强化学习的招标策略的绩效的影响。最后，我们总结了使用强化学习算法优化招标策略的一般步骤，并提出了我们的建议。
Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.
