模拟罕见的事件:在线广告定位使用机器学习和数据挖掘Modeling rare events: online advertisement targeting using machine learning and data mining |
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课程网址: | http://videolectures.net/mmdss07_shanahan_mre/ |
主讲教师: | James G. Shanahan |
开课单位: | 丘奇和邓肯集团 |
开课时间: | 2008-11-19 |
课程语种: | 英语 |
中文简介: | Turn自动定位网络为广告商提供了一个革命性的在线广告活动选择(根据互动广告局,2006年在线广告是一个价值160亿美元的行业)。广告客户只需将其广告投放到自助服务控制台中,而Turn则会完成剩下的工作。与当今运营的许多广告网络不同,Turn融合了机器学习,信息科学和统计领域的广泛行业专业知识和创新技术,真正使在线广告无风险,相关,简单,有效,最重要的是,有利可图。与广告客户需要员工团队管理人工定位(包括选择网站或选择和优化数十万个关键字)的传统广告网络不同,Turn网络会自动分析和定位广告。 Turn’ s技术可动态选择和混合数百个变量,如过去的表现,品牌强度,用户档案,行动类型和网站类别,以确定每个广告的最佳目标,从而消除猜测,时间和复杂性。 Turn网络基于统计技术,智能地定位文本广告和图形广告。通过动态自动选择和混合定位变量,Turn可以确定适用于任何情况的最佳广告或广告组。 Turn凭借其独特的出价CPA模式提供真正的付费表现。由于广告客户为其定义的操作付费,因此Turn可消除无价值或欺诈性点击的风险。无论广告客户是为产品购买,网站访问,潜在客户或电子邮件注册付费,广告客户都可以控制他们支付的费用以及何时支付费用。在此问题设置的背景下(有数十亿次广告展示),本海报将解决使用机器学习和数据挖掘建模稀有事件的一些关键问题,如不确定性,回归与分类困境和特征工程。 |
课程简介: | The Turn automatic targeting network provides advertisers a revolutionary option for online advertising campaigns (online advertising is a $16 billion industry in 2006 according to the Interactive Advertising Bureau). An advertiser simply inputs its ad into a self-serve console, and Turn does the rest. Unlike many ad networks operating today, Turn incorporates extensive industry expertise and innovative technology from the fields of machine learning, information science and statistics, to truly make online advertising risk-free, relevant, simple, effective, and most importantly, profitable. Unlike traditional ad networks where advertisers need teams of employees to manage manual targeting including selecting sites or selecting and optimizing hundreds of thousands of keywords, the Turn network automatically analyzes and targets ads. Turn’s technology dynamically selects and blends hundreds of variables such as past performance, brand strength, user profiles, action type and site categories to determine the best targets for each ad, thus eliminating guesswork, time and complexity. The Turn network is based on statistical technology that intelligently targets both text and graphical ads. By dynamically and automatically selecting and blending targeting variables, Turn can determine the best ad or group of ads for any situation. Turn offers true pay-forperformance with its unique bidded CPA model. Because advertisers pay for actions that they define, Turn eliminates the risk of worthless or fraudulent clicks. Whether an advertiser is paying for product purchases, site visits, leads, or email signups, the advertiser is in control of what they pay for and when they pay for it. In the context of this problem setting (with billions of ad impressions), this poster will address some key issues in modeling rare events using machine learning and data mining such as uncertainty, the regression versus classification dilemma and feature engineering. |
关 键 词: | 广告; 信息科学与统计; 在线广告 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-13:zyk |
阅读次数: | 99 |