arXiv 1908.089362019-08cs.CYcs.LGstat.ML

可感知广告创意选择

Fatigue-Aware Ad Creative Selection

CyberAgent, Inc.（赛博艾坚特公司）；Keio University（庆应义塾大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）

Daisuke Moriwaki, Komei Fujita, Shota Yasui, Takahiro Hoshino

备注：: The previous version was uploaded under the title of "A Contextual Bandit for Ad Creative Selection under Ad Fatigue"

摘要：

在在线展示广告中，为每次展示选择最有效的广告创意（广告图像）是DSP（需求方平台）实现其目标（点击率，转换数量，收入和品牌提升）的关键任务。正如营销文献中广泛认识到的那样，广告创意的效果随着重复广告曝光的数量而变化。在这项研究中，我们提出了一个有效的和易于实现的广告创意选择算法，明确考虑用户的心理状态时，选择广告创意。所提出的系统部署在一个真实的生产环境中，并对基线算法进行了测试。实验结果表明了该算法的优越性。

英文摘要：

In online display advertising, selecting the most effective ad creative (ad image) for each impression is a crucial task for DSPs (Demand-Side Platforms) to fulfill their goals (click-through rate, number of conversions, revenue, and brand improvement). As widely recognized in the marketing literature, the effect of ad creative changes with the number of repetitive ad exposures. In this study, we propose an efficient and easy-to-implement ad creative selection algorithm that explicitly considers user's psychological status when selecting ad creatives. The proposed system was deployed in a real-world production environment and tested against the baseline algorithms. The results show superiority of the proposed algorithm.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。