为相似的观众打分

Qiang Ma, Eeshan Wagh, Jiayi Wen, Zhen Xia, Róbert Ormándi, Datong Chen
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引用次数: 16

摘要

相似模式是一种从较小的用户群中寻找相似用户的有效工具,它正在迅速改变在线程序化广告行业。这些上下文中的数据集在大规模上表现出极其稀疏的特征空间,因此传统上,最先进的外观相似模型使用成对相似性来构建这些相似的用户集。基于相似性的模型的主要挑战之一是,它们不能提供一种方法来衡量用户对广告商的潜在价值,而这在广告环境中是至关重要的。我们提出的方法,以一种方式,直接关系到业务指标,广告商想要优化扩大的受众评分用户。我们提出了三种评分模型,并表明,通过使用真实世界的大规模数据进行实证评估,通过将用户对广告商的潜在价值纳入我们的评分模型,我们可以显着提高相似模型的性能,而不是仅使用用户的两两相似性的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Score Look-Alike Audiences
Look-alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state-of-the-art look-alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity-based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real-world, large-scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look-alike models over methods which only use pairwise similarities of users.
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