揭示广告关键词推荐中的中介偏差，提升与搜索相关性的对齐效果。
Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
- 将广告与搜索视为动态系统，重新定义关键词相关性
- 发现搜索系统偏差导致广告关键词推荐失准
- 验证交叉编码器在对齐效果上优于双编码器，适合平台级应用
电商平台为卖家推荐关键词以提升买家参与度（点击/销售）。关键词需与商品相关，否则引发卖家不满和低效投放。为此采用相关性过滤器。本文指出，基于有偏点击/销售信号训练相关性模型存在缺陷。我们重新将广告关键词相关性理解为广告系统与搜索系统（作为触达买家的中介）之间的交互。分析了搜索相关性系统的偏差（中介偏差），强调需使广告关键词与搜索相关性信号对齐。还比较了交叉编码器与双编码器在建模对齐效果上的表现，并评估了该方案在eBay卖家规模下的可扩展性。
原文摘要 · Abstract (English)
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.
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