文章基本信息

标题：Predicting Clicks: Estimating the Click-Through Rate for New Ads
本地全文：下载
作者：Matthew Richardson ; Ewa Dominowska ; Robert Ragno 等
期刊名称：Revista Paradoxos
电子版ISSN：2525-5568
出版年度：2019
卷号：3
期号：1
页码：32-45
DOI：10.14393/par-v3n1-2018-49692
出版社：Universidade Federal de Uberlândia
摘要：Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.
关键词：click-through rate; sponsored search; paid search; Web advertising; CTR; CPC; ranking.