In the era of online advertising, the new norm of mobile-app advertising defines a novel revenue generation channel for publishers through user- and context-targeted advertisements. Unlike online advertising, mobile-app advertising has unique behaviors due to certain constraints and attributes. As a result, the existing solutions of return optimization for publishers do not always provide the expected outcome. This has created a new research gap. Finding a solution at the app instance level of the mobile advertising ecosystem has a high potential to bridge this gap. This study provides a full-fledged mechanism to determine the ad network, which gains the highest return to the publisher at the app instance level based on the attributes of both the advertisement and the ad network. Using such attributes and the mobile-app user's click behavior, we estimate the ad network effectiveness, the advertisement effectiveness, and the click-through rate to determine the optimal ad network, which provides the highest return for the publisher. Through a simulation experiment based on data generated in real-life scenarios, we demonstrate that the publisher-return is higher in our proposed approach than that obtained from advertisements from a single ad network for all the mobile-app users.

中文翻译：

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用于移动应用广告中发布商回报优化的自适应广告网络选择

在在线广告时代，移动应用广告的新规范通过针对用户和上下文的广告为发布商定义了新的创收渠道。与在线广告不同，移动应用广告由于某些限制和属性而具有独特的行为。因此，现有的出版商回报优化解决方案并不总能提供预期的结果。这创造了一个新的研究空白。在移动广告生态系统的应用程序实例级别找到解决方案具有弥合这一差距的巨大潜力。本研究提供了一种成熟的机制来确定广告网络，根据广告和广告网络的属性，在应用程序实例级别为发布者获得最高回报。使用这些属性和移动应用用户的点击行为，我们通过评估广告网络有效性、广告有效性和点击率来确定最佳广告网络，从而为发布商提供最高回报。通过基于现实生活场景中生成的数据的模拟实验，我们证明了我们提出的方法的发布者回报高于所有移动应用用户从单个广告网络的广告获得的回报。