Information Technology and Quantitative Management ( ITQM 2013 ) Advertisement Click-Through Rate Prediction using Multiple Criteria Linear Programming Regression Model
Information Technology and Quantitative Management ( ITQM 2013 ) Advertisement Click-Through Rate Prediction using Multiple Criteria Linear Programming Regression Model
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发表时间：
2013
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通讯作者：
Fang Wang;W. Suphamitmongkol;Bo Wang
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作者：
Fang Wang;W. Suphamitmongkol;Bo Wang
在广告行业中，重要的是预测将点击目标广告的潜在盈利用户（即，行为目标）。该任务通过分析用户的点击/网页浏览信息并向他们显示最相关的广告来选择可能点击广告的潜在用户。在本文中，我们提出了一个多目标线性规划回归（MCLPR）预测模型作为解决方案。实验数据集由中国一家领先的互联网公司提供，可从KDD Cup 2012数据集的track 2下载。在本文中，支持向量回归（SVR）和逻辑回归（LR）被用作两个基准模型进行比较。结果表明，MCLPR是一个很有前途的模型在行为目标任务。
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.