广告中的中间商偏差：使关键词推荐的相关性与搜索对齐
Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
AI总结：
本文揭示广告关键词推荐中搜索系统作为中间商的偏差，提出将广告与搜索视为动态系统交互，并比较交叉编码器与双编码器以对齐相关性信号，提升eBay卖家广告效果。
摘要：
电子商务卖家根据其库存被推荐关键词，并基于这些关键词进行广告投放，以增加买家参与度（点击/销售）。关键词必须与商品相关；否则，可能导致卖家不满和定位不佳——为此，采用了相关性过滤器。在这项工作中，我们描述了在偏倚的点击/销售信号上训练相关性过滤器模型的缺陷。我们将广告主关键词相关性重新概念化为两个动态系统之间的交互——产生关键词的广告系统和作为中间商接触买家的搜索系统。我们讨论了搜索相关性系统的偏差（中间商偏差），以及将广告主关键词与搜索相关性信号对齐的必要性。我们还比较了交叉编码器和双编码器在建模这种对齐方面的性能，以及这种解决方案在eBay卖家中的可扩展性。
英文摘要：
E-commerce sellers are recommended keyphrases based on their inventory on which they advertise to increase buyer engagement (clicks/sales). Keyphrases must be pertinent to items; otherwise, it can result in seller dissatisfaction and poor targeting -- towards that end relevance filters are employed. In this work, we describe the shortcomings of training relevance filter models on biased click/sales signals. We re-conceptualize advertiser keyphrase relevance as interaction between two dynamical systems -- Advertising which produces the keyphrases and Search which acts as a middleman to reach buyers. We discuss the bias of search relevance systems (middleman bias) and the need to align advertiser keyphrases with search relevance signals. We also compare the performance of cross encoders and bi-encoders in modeling this alignment and the scalability of such a solution for sellers at eBay.
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。
