基于内容广告平台的点击率预估系统的设计与实现

发布时间：2018-02-16 19:13

本文关键词： 内容广告广告相关性点击率预估广告排序逻辑回归　出处：《南京大学》2012年硕士论文　论文类型：学位论文

【摘要】：内容广告是互联网广告的一种,基于分析页面内容和用户信息将高相关性广告展现在网页上。内容广告系统与传统互联网广告系统有着很大的不同,内容广告系统主要将小广告主的广告展现在长尾流量上,因此,内容广告系统的广告库更大,流量也更多。在内容广告系统中,每次广告检索都是从百万级的广告库中挑选与页面、用户信息最相关的部分广告,由于性能原因,无法运用复杂的技术逐一计算每条广告的相关性,所以,内容广告系统按照相关性计算的复杂度将检索过程分成两个部分：广告粗选和广告排序。广告粗选阶段采用计算量较小的技术挑选部分广告,然后在广告排序阶段运用复杂的分析技术对这部分广告进行排序。本文主要关注广告排序阶段,即点击率预估。传统计算相关性的方法是提取广告和页面的关键词向量,计算两个向量的相似度,这种方法最大的缺点是忽略了广告展示和点击的历史日志。本文介绍的点击率预估系统通过提取广告、用户和页面信息的特征,运用逻辑回归模型预估广告点击率,并基于此对广告进行排序,逻辑回归模型从线下广告历史日志中训练得出。相对于传统方法,点击率预估技术利用的信息更加全面,从历史日志中挖掘信息训练模型也使得相关性计算更加准确。本文主要介绍了点击率预估系统的设计与实现。首先介绍了国内外计算广告相关性的各种方法,引出了点击率预估,然后介绍了点击率预估的算法原理和在实现点击率预估系统的过程中使用的主要技术。在后续章节中,通过对内容广告系统的整体架构以及设计思想的分析,引出了点击率预估的需求包括功能、性能和内外部接口。围绕着需求展开了对点击率预估系统的设计与实现的介绍,并着重在性能和算法实验的便捷性两个方面做了重点的分析优化。最后详细分析了点击率预估系统对整个内容广告系统带来的效果提升。论文的最后,通过总结与展望,对技术的改进方向以及应用前景做了进一步的分析。
[Abstract]:Content advertising is a kind of Internet advertising, which is based on analyzing page content and user information to display highly relevant ads on web pages. Content advertising systems are very different from traditional Internet advertising systems. The content advertising system mainly displays the small advertisers' advertisements on the long tail flow, so the content advertising system has a larger advertising base and more traffic. In the content advertising system, Each advertising retrieval is a selection of pages from the millions of ad libraries, the most relevant part of user information, because of performance reasons, can not use complex technology to calculate the relevance of each ad, so, The content advertising system divides the retrieval process into two parts according to the complexity of correlation calculation: ad selection and advertisement sorting. Then we use the complex analysis technology to sort this part of advertisements in the advertising sequencing stage. This paper mainly focuses on the advertising sequencing stage, that is, the prediction of click rate. The traditional method to calculate the correlation is to extract the keyword vector of the advertisement and the page, and calculate the similarity between the two vectors. The biggest drawback of this method is that it ignores the historical log of advertising display and click. The click rate prediction system introduced in this paper uses the logical regression model to estimate the ad click rate by extracting the features of advertisement, user and page information. The logical regression model is trained from the offline advertising history log. Compared with the traditional method, the information used by the click rate estimation technology is more comprehensive. Mining information training model from history log also makes correlation calculation more accurate. This paper mainly introduces the design and realization of the prediction system of click rate. Firstly, it introduces various methods of calculating the correlation of advertisement at home and abroad, and leads to the prediction of click rate. Then it introduces the algorithm principle and main technology used in the process of realizing the prediction system of click rate. In the following chapters, through the analysis of the whole structure and design idea of the content advertising system, The requirements for the prediction of click rate include function, performance and internal and external interfaces. The design and implementation of the system are introduced around the demand. The performance and the convenience of algorithm experiment are analyzed and optimized in detail. Finally, the effect of the click rate prediction system on the whole content advertising system is analyzed in detail. The improvement direction and application prospect of the technology are further analyzed.
【学位授予单位】：南京大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TP311.52

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