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摘要翻译：
本文对网络广告归因分析中的Shapley值方法进行了重新研究。Shapley值法作为合作博弈论中的信用分配解决方案，直接量化了网络广告投入在多个渠道对广告关键绩效指标(KPI)的贡献。我们通过发展一个替代的数学公式简化了它的计算。新公式显著提高了计算效率，从而扩大了适用范围。在简化公式的基础上，我们进一步发展了有序Shapley值方法。该方法能够考虑用户访问信道的顺序。我们声称，它提供了一个更全面的洞察力，通过评估在用户转换旅程的不同阶段的渠道归属。使用一个真实世界的在线广告活动数据集对所提出的方法进行了说明。
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英文标题：
《Shapley Value Methods for Attribution Modeling in Online Advertising》
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作者：
Kaifeng Zhao, Seyed Hanif Mahboobi, Saeed R. Bagheri
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
This paper re-examines the Shapley value methods for attribution analysis in the area of online advertising. As a credit allocation solution in cooperative game theory, Shapley value method directly quantifies the contribution of online advertising inputs to the advertising key performance indicator (KPI) across multiple channels. We simplify its calculation by developing an alternative mathematical formulation. The new formula significantly improves the computational efficiency and therefore extends the scope of applicability. Based on the simplified formula, we further develop the ordered Shapley value method. The proposed method is able to take into account the order of channels visited by users. We claim that it provides a more comprehensive insight by evaluating the attribution of channels at different stages of user conversion journeys. The proposed approaches are illustrated using a real-world online advertising campaign dataset.
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PDF链接：
https://arxiv.org/pdf/1804.05327
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