Smart Advertisement for Maximal Clicks in Online Social Networks Without User Data

Nathaniel Hudson, Hana Khamfroush, Brent Harrison, Adam Craig
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引用次数: 2

Abstract

Smart cities are a growing paradigm in the design of systems that interact with one another for informed and efficient decision making, empowered by data and technology, of resources in a city. The diffusion of information to citizens in a smart city will rely on social trends and smart advertisement. Online social networks (OSNs) are prominent and increasingly important platforms to spread information, observe social trends, and advertise new products. To maximize the benefits of such platforms in sharing information, many groups invest in finding ways to maximize the expected number of clicks as a proxy of these platform's performance. As such, the study of click-through rate (CTR) prediction of advertisements, in environments like online social media, is of much interest. Prior works build machine learning (ML) using user-specific data to classify whether a user will click on an advertisement or not. For our work, we consider a large set of Facebook advertisement data (with no user data) and categorize targeted interests into thematic groups we call conceptual nodes. ML models are trained using the advertisement data to perform CTR prediction with conceptual node combinations. We then cast the problem of finding the optimal combination of conceptual nodes as an optimization problem. Given a certain budget $k$, we are interested in finding the optimal combination of conceptual nodes that maximize the CTR. We discuss the hardness and possible NP-hardness of the optimization problem. Then, we propose a greedy algorithm and a genetic algorithm to find near-optimal combinations of conceptual nodes in polynomial time, with the genetic algorithm nearly matching the optimal solution. We observe that simple ML models can exhibit the high Pearson correlation coefficients w.r.t. click predictions and real click values. Additionally, we find that the conceptual nodes of “politics”, “celebrity”, and “organization” are notably more influential than other considered conceptual nodes.
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在没有用户数据的在线社交网络中获得最大点击量的智能广告
智能城市是一种不断发展的系统设计范式,这些系统相互作用,通过数据和技术,对城市资源进行知情和有效的决策。在智慧城市中,信息向市民的传播将依赖于社会趋势和智能广告。在线社交网络(Online social network,简称osn)是信息传播、社会趋势观察和新产品宣传的重要平台。为了最大限度地发挥这些平台在信息共享方面的优势,许多组织投资于寻找最大化预期点击次数的方法,以此作为这些平台性能的代理。因此,在像在线社交媒体这样的环境中,对广告点击率(CTR)预测的研究非常有趣。之前的工作使用特定于用户的数据构建机器学习(ML),以分类用户是否会点击广告。在我们的工作中,我们考虑了大量的Facebook广告数据(没有用户数据),并将目标兴趣分类为我们称之为概念节点的主题组。使用广告数据训练ML模型,使用概念节点组合执行CTR预测。然后,我们将寻找概念节点的最优组合的问题转换为优化问题。给定一定的预算$k$,我们感兴趣的是找到最大化点击率的概念节点的最佳组合。我们讨论了优化问题的硬度和可能的np硬度。然后,我们提出了一种贪心算法和一种遗传算法,在多项式时间内找到概念节点的近最优组合,遗传算法几乎匹配最优解。我们观察到,简单的ML模型可以显示高Pearson相关系数w.r.t.点击预测和真实点击值。此外,我们发现“政治”、“名人”和“组织”的概念节点明显比其他考虑的概念节点更有影响力。
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