用搜索结果页广告网络设计实验，解决电商竞价广告测试中的用户匿名难题。
SERP Interference Network and Its Applications in Search Advertising
- 构建查询-广告的双向干扰网络，通过加权投影生成可分组的单部图。
- 在真实竞价算法测试中验证了新方法的有效性，显著提升实验可靠性。
- 适合从事搜索广告优化与因果推断的研究者和工程团队使用。
电商平台的搜索引擎营销团队需通过持续快速的搜索广告A/B测试来优化长期盈利并提升用户搜索体验。然而，由于搜索引擎上用户身份匿名，无法基于用户进行随机化；同时，对多数感兴趣干预措施而言，简单按产品随机化会违反稳定处理值假设。本文提出利用被截断的观测数据构建双部图（搜索查询到产品广告或文字广告）的SERP干扰网络。通过一种新型加权函数，生成加权投影以形成单部图，并据此聚类实现随机化。该方法成功应用于评估新的付费搜索竞价算法。此外，我们提供了一种基于SageMaker的新系统架构蓝图，支持多语言编程实现实验框架的各组件。
原文摘要 · Abstract (English)
Search Engine marketing teams in the e-commerce industry manage global search engine traffic to their websites with the aim to optimize long-term profitability by delivering the best possible customer experience on Search Engine Results Pages (SERPs). In order to do so, they need to run continuous and rapid Search Marketing A/B tests to continuously evolve and improve their products. However, unlike typical e-commerce A/B tests that can randomize based on customer identification, their tests face the challenge of anonymized users on search engines. On the other hand, simply randomizing on products violates Stable Unit Treatment Value Assumption for most treatments of interest. In this work, we propose leveraging censored observational data to construct bipartite (Search Query to Product Ad or Text Ad) SERP interference networks. Using a novel weighting function, we create weighted projections to form unipartite graphs which can then be use to create clusters to randomized on. We demonstrate this experimental design's application in evaluating a new bidding algorithm for Paid Search. Additionally, we provide a blueprint of a novel system architecture utilizing SageMaker which enables polyglot programming to implement each component of the experimental framework.
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