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在赞助搜索个性化点击预测

Personalized Click Prediction in Sponsored Search
课程网址: http://videolectures.net/wsdm2010_cheng_pcpis/  
主讲教师: Haibin Cheng
开课单位: 雅虎硅谷研究院
开课时间: 2010-04-12
课程语种: 英语
中文简介:
赞助搜索是一项价值数十亿美元的业务,可为搜索引擎带来大部分收入。预测用户点击广告的可能性对于赞助搜索至关重要,因为该预测用于影响广告的排名,过滤,展示位置和定价。广告排名,过滤和展示位置会对用户体验产生直接影响,因为用户希望最有用的广告排名靠前,并置于页面的显着位置。定价会影响广告客户’为搜索引擎回报他们的投资和收入。本文的目的是提出一个在赞助搜索中个性化点击模型的框架。我们开发用户特定和基于人口统计的功能,以反映个人和群组的点击行为。这些特征基于对商业搜索引擎的大量用户的搜索和点击行为的观察。我们将这些功能添加到基线非个性化点击模型,并对从用户日志和实时流量派生的离线测试集执行实验。我们的结果表明,个性化模型显着提高了点击预测的准确性。
课程简介: Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers’ return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.
关 键 词: 付费搜索; 个性化的服务模型; 基线
课程来源: 视频讲座网
最后编审: 2020-05-21:王淑红(课程编辑志愿者)
阅读次数: 132