k-匿名下定向在线广告的触达测量、优化与频次封顶
Reach Measurement, Optimization and Frequency Capping In Targeted Online Advertising Under k-Anonymity
AI总结：
本文研究 $k$-匿名隐私约束下定向在线广告的触达测量与优化，提出概率折扣方法改造传统频次封顶，并通过实验量化隐私与广告效果的权衡。
摘要：
近年来，利用在线广告提升品牌认知的增长在很大程度上归因于社交媒体的普及。促成在线品牌广告成功的一项关键技术是频次封顶，这一机制使营销人员能够控制向特定用户展示广告的次数。然而，随着行业转向优先考虑用户隐私的广告解决方案，这项技术的根本基础正受到审视。本文深入研究了 $k$-匿名背景下的触达测量与优化问题，$k$-匿名是一种隐私保护模型，正受到各大在线广告平台的关注。我们概述了在这一新的隐私环境中如何报告触达，并展示了如何采用概率折扣（传统频次封顶的概率化适配版本）来优化广告活动表现。我们开展实验，评估用户隐私与在线品牌广告效果之间的权衡。值得注意的是，我们发现只要引入隐私，表现就会显著下降；但与此同时，广告平台为向用户提供更多隐私所承担的额外成本却较为有限。
英文摘要：
The growth in the use of online advertising to foster brand awareness over recent years is largely attributable to the ubiquity of social media. One pivotal technology contributing to the success of online brand advertising is frequency capping, a mechanism that enables marketers to control the number of times an ad is shown to a specific user. However, the very foundation of this technology is being scrutinized as the industry gravitates towards advertising solutions that prioritize user privacy. This paper delves into the issue of reach measurement and optimization within the context of $k$-anonymity, a privacy-preserving model gaining traction across major online advertising platforms. We outline how to report reach within this new privacy landscape and demonstrate how probabilistic discounting, a probabilistic adaptation of traditional frequency capping, can be employed to optimize campaign performance. Experiments are performed to assess the trade-off between user privacy and the efficacy of online brand advertising. Notably, we discern a significant dip in performance as long as privacy is introduced, yet this comes with a limited additional cost for advertising platforms to offer their users more privacy.
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。
