arXiv上的一篇新研究论文提出了一个框架，用于修正大规模广告系统中的归因错误，特别是针对TikTok等平台。所提出的方法使用增量实验来校准归因输出，解决了付费转化可能因渠道重叠而夸大真实增长的问题。当在TikTok上部署时，该系统帮助将测量的销量抵消率降低了约15个百分点，从而实现了更准确的预算分配和策略调整。 AI
影响 通过修正归因错误，提高大规模广告的投资回报率衡量和预算分配。
排序理由 该集群包含一篇发表在arXiv上的研究论文。
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arXiv:2606.26690v1 Announce Type: cross Abstract: In large-scale paid acquisition and growth advertising systems, production attribution outputs are widely used for daily budget allocation and channel diagnosis. However, paid-attributed conversions such as daily new users (DNU) m…
In large-scale paid acquisition and growth advertising systems, production attribution outputs are widely used for daily budget allocation and channel diagnosis. However, paid-attributed conversions such as daily new users (DNU) may systematically overstate true incremental growt…