互联网广告精准投放平台的研究

发布时间：2018-03-29 08:29

本文选题：精准广告投放　切入点：贝叶斯分类　出处：《华中师范大学》2013年硕士论文

【摘要】：随着网络技术的飞速发展,互联网广告成为互联网企业最重要的盈利手段之一。越来越多的企业和机构开始研究互联网广告平台,与此同时,很多企业也慢慢地开始从传统媒体广告投放转向互联网广告投放。然而,互联网广告投放的随意性和泛滥性让网民深受其烦,不仅网络广告的投放得不到预期的效果,而且网站点击率也随之下降。针对这种情况,互联网广告的精准投放给互联网广告市场带来了无限生机。精准广告投放即针对用户的个性化向其投放感兴趣的广告,同时真正满足用户对产品需求的信息。目前互联网广告系统中,要做到精准投放主要有三种方式：常见的定向型,主要是针对地理位置、投放时间段等单个属性或者组合属性进行投放；另一种是基于内容的投放方式,这种广告投放系统主要包括提取网页主题词、提取广告文本主题词,计算它们之间的相关性,然后进行广告的投放。而基于用户行为特征的精准广告投放系统主要是在提取到用户的行为特征数据之后,深入挖掘用户的特征数据,然后采用合适的分类算法对用户分类,进而针对用户的特征投放广告。本文通过对互联网广告交易模式的进一步分析,实现了一个互联网广告需求方平台即DSP (Demand Side Platform)原型系统,该系统通过与互联网广告交易平台的对接,主要帮助广告主参与到广告的竞拍中,并且综合用户信息、广告信息等各种信息计算最佳待投放的广告,从而实现广告的精准投放。在用分类算法对用户的特征分类时,常见的分类算法有神经网络分类算法、决策树分类算法及贝叶斯分类算法等,但每种算法都有自己的优缺点,通过对比分析,选择贝叶斯算法作为用户特征分类算法。同时,考虑到每个属性对类属性不同的影响程度,运用信息论的相关知识,设计出改进的贝叶斯算法,经过试验对比,改进的贝叶斯算法比朴素贝叶斯算法的算法分类准确率更高。
[Abstract]:With the rapid development of network technology, Internet advertising has become one of the most important means of profit for Internet enterprises. More and more enterprises and institutions begin to study Internet advertising platforms, at the same time, Many enterprises have also slowly begun to shift from traditional media advertising to Internet advertising. However, the randomness and flooding of Internet advertising have upset netizens deeply, not only that the online advertising cannot achieve the desired results, In response to this situation, the precise delivery of Internet advertising has brought infinite vitality to the Internet advertising market. At the same time, truly meet the needs of users for product information. In the current Internet advertising system, there are three main ways to achieve accurate delivery: the common directional type, mainly aimed at the geographical location, time periods and other single attributes or combination attributes; The other is content-based delivery. This advertising system mainly includes extracting the theme words of the web page, extracting the theme words of the advertising text, calculating the correlation between them. The accurate advertising system based on the user behavior features is mainly to extract the user behavior feature data, and then use the appropriate classification algorithm to classify the user. And then to the characteristics of the user advertising. In this paper, a prototype system of DSP demand Side platform is implemented through the further analysis of the mode of Internet advertising transaction, and the system is connected with the Internet advertising trading platform. It mainly helps advertisers to participate in the bidding of advertisements, and synthesizes all kinds of information, such as user information, advertising information and other information to calculate the best ads to be placed, so as to achieve the accurate placement of advertisements. When classifying users' features by using classification algorithms, the common classification algorithms include neural network classification algorithm, decision tree classification algorithm and Bayesian classification algorithm, but each algorithm has its own advantages and disadvantages. The Bayesian algorithm is chosen as the classification algorithm of user characteristics. At the same time, considering the different influence of each attribute on the class attribute, using the relevant knowledge of information theory, the improved Bayesian algorithm is designed and compared through experiments. The improved Bayesian algorithm is more accurate than the naive Bayesian algorithm.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP393.09;TP311.52

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