本文選題：廣告 + 回歸模型　； 參考：《南京大學》2012年碩士論文

【摘要】：廣告是眾多互聯網站點的主要盈利模式之一,隨著互聯網廣告行業(yè)的發(fā)展,有越來越多的網站將廣告位交由廣告聯盟托管。廣告聯盟和多個廣告主簽訂廣告投放計劃,通過一系列匹配算法,將廣告展現在最合適的網站上。廣告普遍按照點擊次數計費,但廣告的點擊率是不確定的,不同廣告的點擊單價也不同。對于網站主來說,一個廣告位未來能獲取的收益是未知的,這在一定程度上影響了網站主的積極性。在這種情況下,如何預測一個廣告位所能獲取的收入就成為一個亟待解決的問題。 本文介紹了一種預測廣告位收益的方法,主要思想是利用大量歷史數據,通過支持向量機來建立一個回歸模型。主要工作分為三個部分。第一,環(huán)境特征抽取,環(huán)境特征通過抓取并分析廣告位所在網頁的內容得到。第二,數據處理,分析所有特征數據的有效性和特征之間的相關性,之后根據模型訓練的需要,對數據進行篩選和轉換。第三,模型訓練與優(yōu)化,模型訓練基于已有的支持向量機算法庫完成,在此基礎上通過參數尋優(yōu)、特征選取、特征綁定等方法做了大量優(yōu)化。本文詳細闡述了這三個階段的工作和成果,并在最后進行了簡單總結,提出了未來可以進行的改進。
[Abstract]:Advertising is one of the main profit models for many Internet sites. With the development of the Internet advertising industry, more and more websites are handing over advertising positions to ad federations.The Advertising Alliance has signed up with multiple advertisers to display ads on the most appropriate site through a series of matching algorithms.Ads generally charge according to the number of clicks, but the click rate is uncertain, and the click price varies from ad to ad.For website owners, the future revenue of an advertising site is unknown, which to some extent affects the enthusiasm of site owners.In this case, how to predict the revenue a advertising position can achieve becomes a problem to be solved.This paper introduces a method to predict the revenue of advertising bit. The main idea is to build a regression model by using a large amount of historical data and support vector machine.The main work is divided into three parts.First, environmental feature extraction, which is obtained by grabbing and analyzing the content of the page in which the advertisement is located.Secondly, data processing analyzes the validity of all feature data and the correlation between features, and then filters and transforms the data according to the needs of model training.Third, model training and optimization, model training based on the existing support vector machine algorithm library, on this basis through parameter optimization, feature selection, feature binding and other methods to do a lot of optimization.In this paper, the work and achievements of these three stages are described in detail, and a brief summary is made at the end of this paper, and some possible improvements in the future are put forward.
【學位授予單位】：南京大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP393.092

【參考文獻】

相關期刊論文 前1條

1 張浩然,韓正之;回歸支持向量機的改進序列最小優(yōu)化學習算法[J];軟件學報;2003年12期

，

本文編號：1758361