图 书 馆
自适应的关键字值计算称重设计Adaptive Weighing Designs for Keyword Value Computation |
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| 课程网址: | http://videolectures.net/wsdm2010_zervas_awdf/ |
| 主讲教师: | Georgios Zervas |
| 开课单位: | 波士顿大学 |
| 开课时间: | 2010-03-18 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Attributing a dollar value to a keyword is an essential part of running any profitable search engine advertising campaign. When an advertiser has complete control over the interaction with and monetization of each user arriving on a given keyword, the value of that term can be accurately tracked. However, in many instances, the advertiser may monetize arrivals indirectly through one or more third parties. In such cases, it is typical for the third party to provide only coarse-grained reporting: rather than report each monetization event, users are aggregated into larger channels and the third party reports aggregate information such as total daily revenue for each channel. Examples of third parties that use channels include Amazon and Google AdSense. In such scenarios, the number of channels is generally much smaller than the number of keywords whose value per click (VPC) we wish to learn. However, the advertiser has flexibility as to how to assign keywords to channels over time. We introduce the channelization problem: how do we adaptively assign keywords to channels over the course of multiple days to quickly obtain accurate VPC estimates of all keywords? We relate this problem to classical results in weighing design, devise new adaptive algorithms for this problem, and quantify the performance of these algorithms experimentally. Our results demonstrate that adaptive weighing designs that exploit statistics of term frequency, variability in VPCs across keywords, and flexible channel assignments over time provide the best estimators of keyword VPCs. |
| 关 键 词: | 自适应; 关键字值计算; 称重设计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-04：cxin |
| 阅读次数: | 58 |