基于逻辑回归模型的广告点击率预估系统的设计与实现

发布时间：2018-06-20 00:28

本文选题：特征选择 + 广告点击率　；参考：《哈尔滨工业大学》2016年硕士论文

【摘要】：如今互联网变现和收入的主要来源是广告,像Google、Yahoo、Facebook等靠广告收入的公司已经走在互联网广告行业的前沿。所以如何研究广告变现、如何提高广告的收入是现在国内外各大互联网研究和人才竞争的热点。其中,互联网广告中的移动广告与传统的广告在广告收费和广告展现上都有着本质的区别。移动广告主要研究的是如何从更大更多的流量和数以百万计的广告库中选取合适的广告对用户进行精准的投放。但是如何在这样复杂而且需要消耗高性能为代价的环境中对广告进行排序,以及如何设计和实现这样一个能对每个候选广告计算点击率大小的系统是本文研究的主要工作。以上提出的是基于用户当时所触发的环境,利用用户本身的信息和用户浏览的页面信息,对相关的广告计算被该用户点击的概率大小,从而按照计算的概率对广告排序,选取点击率最大的广告展现给用户,这就是广告点击率(Click-Through Rate,CTR)的预估。以CTR的大小对候选广告排序做了一件一石三鸟的事情,对广告主来说提高了广告的传播;对广告投放公司来说提高了他们的收入;对用户来说提高了体验。与传统的广告排序和投放相比,移动广告有着更全面的信息、更复杂的环境、更准确的预估和投放。本文主要研究CTR预估系统的设计与实现,对特征的选择进行了详细的阐述,对算法模块进行了优化和实验对比,具体的步骤分为:首先,提出CTR预估系统的总体设计,功能和非功能的需求分析;然后,提出CTR预估系统实现的技术方案和评价指标;其次,将CTR预估系统分为线上和线下两个模块分别进行了实现;最后,对特征选择模块首次加入了时间因子的衰减指数和加入APP的分类信息,并分别提出了基于逻辑回归算法、贝叶斯算法、支持向量机算法、融合(梯度提升树加逻辑回归)算法的CTR预估,并通过实验对比优化和线下的测试,使本文提出的CTR预估系统效果得以提升。
[Abstract]:Advertising is now the main source of Internet cash and revenue, and companies such as Google Yahoo Facebook are already at the forefront of the Internet advertising industry. Therefore, how to study advertising realization and how to increase advertising revenue are the hot spots of Internet research and talent competition at home and abroad. Among them, mobile advertising in Internet advertising and traditional advertising in advertising charges and advertising display have essential differences. The main research of mobile advertising is how to select appropriate ads from more and more traffic and millions of ad stores to deliver the users accurately. However, how to sort advertisements in such a complex and high-performance environment, and how to design and implement such a system which can calculate the click rate for each candidate advertisement is the main work of this paper. The above proposal is based on the environment triggered by the user at that time, using the user's own information and the page information viewed by the user to calculate the probability of the advertisement being clicked by the user, so as to sort the advertisement according to the calculated probability. Select the most click-through ads to display to the user, which is the estimate of Click-Through rate (CTR). By the size of the CTR to list the candidate ads to do one thing three birds, for advertisers to increase the spread of advertising; for advertising companies to increase their income; for users to improve the experience. Compared with the traditional advertising ordering and delivery, mobile advertising has more comprehensive information, more complex environment, more accurate prediction and delivery. This paper mainly studies the design and implementation of CTR prediction system, describes the selection of features in detail, optimizes the algorithm module and compares the experimental results. The specific steps are as follows: first, the overall design of CTR prediction system is proposed. Functional and non-functional requirements analysis; then, the CTR prediction system implementation of the technical scheme and evaluation indicators; secondly, the CTR prediction system is divided into online and offline modules are implemented; finally, The attenuation index of time factor and the classification information of app are added to the feature selection module for the first time, and the logical regression algorithm, Bayesian algorithm and support vector machine algorithm are proposed, respectively. The CTR prediction of the fusion (gradient lifting tree plus logical regression) algorithm is improved by comparison and optimization of experiments and off-line test, which can improve the effectiveness of the CTR prediction system proposed in this paper.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP311.52

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