本文選題：增量學習　切入點：精準廣告　出處：《山西財經(jīng)大學》2010年碩士論文　論文類型：學位論文

【摘要】： 網(wǎng)絡技術的飛速發(fā)展,廣告成為網(wǎng)絡盈利的一個主要手段。網(wǎng)絡廣告為越來越多的企業(yè)和機構所了解,并且大部分企業(yè)和機構都進行了網(wǎng)絡廣告的投放。但是,網(wǎng)絡廣告形式多樣,具有動態(tài)性。網(wǎng)絡廣告投放的隨意性和泛濫性,使網(wǎng)絡用戶應接不暇,產(chǎn)生了厭煩的情緒,使網(wǎng)絡廣告的投放達不到預期的效果。針對這種情況,出現(xiàn)了精準廣告投放這一概念。精準廣告投放即向網(wǎng)絡用戶投放其感興趣的,并真正能夠及時供用戶所需的廣告信息。而通過對用戶的行為分析,對用戶進行智能地分類,成為實現(xiàn)精準廣告投放的有效方法,那么對網(wǎng)絡上的海量用戶進行智能分類就成為進行精準廣告投放的重要研究內容。 由于海量的網(wǎng)絡用戶信息,采用數(shù)據(jù)挖掘方法,可以有效地對用戶進行智能分類。然而由于所獲得信息的實時性與在線性,即在互聯(lián)網(wǎng)下的用戶的行為信息是不斷變化的,致使這些海量的信息并不能一次性全部獲得。而對于分批獲得數(shù)據(jù),一般的分類算法,需要不斷地重新的更新分類模型,而耗費了大量的時間。增量學習則是對訓練數(shù)據(jù)集的學習過程逐步展開,后續(xù)的學習結果是建立在先前學習結果的基礎上的。同時,鑒于貝葉斯分類方法能夠充分利用先驗知識學習這一特點,在一定程度上解決了先驗信息傳遞的問題。 因此,本文提出一種基于貝葉斯的增量學習算法用于對精準廣告投放系統(tǒng)中用戶的分類,它是一個利用樣本知識來修正當前知識的連續(xù)的、動態(tài)的過程。通過對在線用戶的行為特征不斷進行分析,根據(jù)獲得的數(shù)據(jù)信息利用貝葉斯增量學習對用戶進行分類,不斷地更新分類模型,以達到更好的分類效果,從而更有效地實現(xiàn)精準廣告的投放。本文選擇了Book-Crossing(BX)數(shù)據(jù)集作為實驗研究對象,它包括278858個用戶(匿名但有人口統(tǒng)計信息),提供了對于271379本圖書的1149780評分信息。通過利用SQL Server 2005處理成實驗所需的數(shù)據(jù)格式。研究結果表明,增量學習對于在線的實時學習能夠解決其他分類器所帶來的時間與精力的耗費,并能獲得較好的分類效果,從而精確地、及時地對網(wǎng)絡用戶的分類,達到精準廣告投放的目的。本文對精準廣告投放系統(tǒng)進行了設計,從系統(tǒng)分析到系統(tǒng)功能的實現(xiàn)都進行了論述,并提出主要是將用戶推薦模塊作為一個通用接口,不僅在本系統(tǒng)中可以應用,在其他的網(wǎng)站中,只要將代碼嵌入并做相應的調整,就同樣可以實現(xiàn)對當下網(wǎng)站的用戶的分類。本文結論部分對整個文章進行了總結,并提出下一步工作。
[Abstract]:With the rapid development of network technology, advertising has become one of the main means of making profits on the Internet. More and more enterprises and institutions have learned about online advertising, and most of them have carried out online advertising. However, Network advertisement has various forms and is dynamic. The randomness and flood of network advertisement put in, make the network user be overwhelmed, produce weariness mood, make the network advertisement put in can't reach the expected effect. In view of this kind of situation, The concept of precision advertising has emerged. Precision advertising, that is, delivering information that is of interest to users on the Internet and can really provide users with the advertising information they need in a timely manner, can be intelligently classified by analyzing the behavior of users. It has become an effective method to achieve precision advertising, so the intelligent classification of mass users on the network becomes an important research content of precision advertising. Because of the huge amount of network user information, using data mining method, users can be classified intelligently. However, because of the real-time and linearity of the information obtained, that is, the behavior information of users under the Internet is constantly changing. As a result, these huge amounts of information can not be obtained all at once. However, for batch data, general classification algorithms need to constantly update the classification model. Incremental learning is the gradual expansion of the learning process of the training data set, and the subsequent learning results are based on the previous learning results. At the same time, In view of the fact that Bayesian classification method can make full use of the characteristics of prior knowledge learning, the problem of prior information transmission is solved to a certain extent. Therefore, an incremental learning algorithm based on Bayesian is proposed to classify the users in the precision advertisement delivery system, which uses sample knowledge to modify the continuity of current knowledge. Dynamic process. Through the continuous analysis of the behavior characteristics of online users, according to the obtained data information, Bayesian incremental learning to classify users, constantly update the classification model, in order to achieve a better classification effect, In this paper, the Book-Crossing BX data set is selected as the experimental research object. It includes 278,858 users (anonymous but demographically available, provides 1149780 rating information for 271379 books). It is processed into experimental data formats using SQL Server 2005. Incremental learning for online real-time learning can solve the cost of time and energy brought by other classifiers, and can obtain better classification effect, thus accurately and timely classification of network users. In this paper, the precision advertising delivery system is designed, from the system analysis to the realization of the system functions are discussed, and it is proposed that the user recommendation module as a general interface, Not only can it be applied in this system, but also in other websites, as long as the code is embedded and adjusted accordingly, it can also realize the classification of the users of the current website. And put forward the next work.
【學位授予單位】：山西財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2010
【分類號】：TP311.13

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