展示廣告中點擊率預估問題研究
發(fā)布時間：2018-10-10 11:02
【摘要】：隨著互聯(lián)網(wǎng)技術的發(fā)展以及隨之而來的信息高流動性,互聯(lián)網(wǎng)廣告成為了商家推崇的一種主流營銷方式,廣告收入也已經(jīng)成為互聯(lián)網(wǎng)公司收入的重要組成部分。廣告點擊率(Click Through Rate,簡稱CTR)預估在精準廣告投放過程中扮演了很重要的角色,預估的準確性對廣告主的收益、廣告商的收益以及用戶的友好體驗有著重大的影響,因此受到互聯(lián)網(wǎng)企業(yè)的廣泛關注。在本文中,我們重點關注展示廣告(Display Advertising),系統(tǒng)地介紹和分析了在線廣告系統(tǒng)的組織結構以及參與對象,闡述了廣告點擊率預估在廣告系統(tǒng)中的重要地位。本文重點關注廣告系統(tǒng)中點擊率預估三個方面的問題。第一個是統(tǒng)一特征平臺的構建。考慮到數(shù)據(jù)有多個不同的來源,數(shù)據(jù)內(nèi)容也包含多個組成部分,如何從原始數(shù)據(jù)中提取出有用的特征并高效地將這些信息進行整合供算法使用有很大的改進空間。本文提出了在真實應用場景中體系化地構建特征、做好特征工程工作的方法,可以從不同的原始日志信息中提取出有用的特征,構建出相對干凈的數(shù)據(jù)特征集合。第二個是高效點擊率預估模型的提出�，F(xiàn)有的工作已有很多將機器學習算法應用到點擊率預估中,但是現(xiàn)有的模型多以線性模型為主,無法建模出廣告信息與用戶信息間的關聯(lián)關系,模型的改進有很大的空間。本文提出了對偶群稀疏模型,構建出廣告系統(tǒng)參與對象間的關聯(lián)關系,從而改進點擊率預估的準確性,同時能夠在所有特征中做一個特征選擇,以促進高效的特征工程工作以及快速的線上預測工作。第三個是大規(guī)模應用場景下的分布式算法實現(xiàn)以及應用�，F(xiàn)實應用場景中,存在著數(shù)據(jù)量大、計算量大的問題,本文提出了基于MPI(Message Passing Interface)的算法分布式實現(xiàn),使得模型能夠充分利用計算集群資源去從海量的數(shù)據(jù)中學習出準確的模型,從而在真實場景中得到應用。
[Abstract]:With the development of Internet technology and the high mobility of information, Internet advertising has become a mainstream marketing method, and advertising revenue has become an important part of the revenue of Internet companies. Ad click rate (Click Through Rate,) prediction plays a very important role in the process of accurate advertising. The accuracy of the prediction has a significant impact on the advertisers' income, advertisers' earnings and the friendly experience of the users. Therefore receives the Internet enterprise's widespread concern. In this paper, we focus on the introduction and analysis of the organizational structure and the participating objects of the online advertising system based on (Display Advertising), and the important position of the ad click rate prediction in the advertising system. This paper focuses on three aspects of the prediction of click rate in advertising system. The first is the construction of unified feature platform. Considering that the data has many different sources and the data content also contains many components, how to extract the useful features from the raw data and efficiently integrate the information for the use of the algorithm has great room for improvement. In this paper, a method of systematically constructing features in real application scenarios and doing well in feature engineering is proposed, which can extract useful features from different original log information and construct relatively clean data feature sets. The second is the high-efficiency click rate prediction model. A lot of existing work has applied machine learning algorithm to the prediction of click rate, but most of the existing models are linear models, which can not model the relationship between advertising information and user information, so there is a lot of room for improvement of the model. In this paper, a sparse dual group model is proposed to construct the correlation relationship between the objects involved in the advertising system, so as to improve the accuracy of the prediction of the click rate, and at the same time to make a feature selection among all the features. In order to promote efficient feature engineering and fast online prediction work. The third is the implementation and application of distributed algorithms in large scale application scenarios. In the practical application scene, there are many problems, such as large amount of data and large amount of computation. This paper proposes a distributed algorithm based on MPI (Message Passing Interface), which makes the model make full use of the computing cluster resources to learn the exact model from the massive data. In order to be used in the real scene.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F713.8
[Abstract]:With the development of Internet technology and the high mobility of information, Internet advertising has become a mainstream marketing method, and advertising revenue has become an important part of the revenue of Internet companies. Ad click rate (Click Through Rate,) prediction plays a very important role in the process of accurate advertising. The accuracy of the prediction has a significant impact on the advertisers' income, advertisers' earnings and the friendly experience of the users. Therefore receives the Internet enterprise's widespread concern. In this paper, we focus on the introduction and analysis of the organizational structure and the participating objects of the online advertising system based on (Display Advertising), and the important position of the ad click rate prediction in the advertising system. This paper focuses on three aspects of the prediction of click rate in advertising system. The first is the construction of unified feature platform. Considering that the data has many different sources and the data content also contains many components, how to extract the useful features from the raw data and efficiently integrate the information for the use of the algorithm has great room for improvement. In this paper, a method of systematically constructing features in real application scenarios and doing well in feature engineering is proposed, which can extract useful features from different original log information and construct relatively clean data feature sets. The second is the high-efficiency click rate prediction model. A lot of existing work has applied machine learning algorithm to the prediction of click rate, but most of the existing models are linear models, which can not model the relationship between advertising information and user information, so there is a lot of room for improvement of the model. In this paper, a sparse dual group model is proposed to construct the correlation relationship between the objects involved in the advertising system, so as to improve the accuracy of the prediction of the click rate, and at the same time to make a feature selection among all the features. In order to promote efficient feature engineering and fast online prediction work. The third is the implementation and application of distributed algorithms in large scale application scenarios. In the practical application scene, there are many problems, such as large amount of data and large amount of computation. This paper proposes a distributed algorithm based on MPI (Message Passing Interface), which makes the model make full use of the computing cluster resources to learn the exact model from the massive data. In order to be used in the real scene.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：F713.8
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