Advertisement Click-Through Rate Prediction Using Multiple Criteria Linear Programming Regression Model

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• 作者：Fang Wang ; ^{fangwangyouxiang@163.com} ; Warawut Suphamitmongkol ^{aapwws@ku.ac.th} ; Bo Wang ^{wangbo8014@126.com}
• 关键词：Behavior Targeting ; Multiple Criteria Linear Programming Regression (MCLPR) ; Support Vector Regression (SVR) ; Logistic Regression (LR)
• 刊名：Procedia Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：17
• 期：Complete
• 页码：803-811
• 全文大小：379 K

文摘

In advertisement industry, it is important to predict potentially profitable users who will click target ads (i.e., Behavioral Targeting). The task selects the potential users that are likely to click the ads by analyzing user's clicking/web browsing information and displaying the most relevant ads to them. In this paper, we present a Multiple Criteria Linear Programming Regression (MCLPR) prediction model as the solution. The experiment datasets are provided by a leading Internet company in China, and can be downloaded from track2 of the KDD Cup 2012 datasets. In this paper, Support Vector Regression (SVR) and Logistic Regression (LR) are used as two benchmark models for comparison. The results indicate that MCLPR is a promising model in behavioral targeting tasks.