Advertisement clicking prediction by using multiple criteria mathematical programming

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In online 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. This paper proposes four multiple criteria mathematical programming models for advertisement clicking problems. First two are multi-criteria linear regression (MCLR) and kernel-based multiple criteria regression (KMCR) algorithms for click-through rate (CTR) prediction. The second two are multi-criteria linear programming (MCLP) and kernel-based multiple criteria programming (KMCP) algorithms, which are used to predict ads clicking events, such as identifying clicked ads in a set of ads. Using the experimental datasets from KDD Cup 2012, the paper first conducts a comparison of the proposed MCLR and KMCR with the methods of support vector regression (SVR) and logistic regression (LR), which shows that both MCLR and KMCR are good alternatives. Then the paper further studies the performance between the proposed MCLP and KMCP algorithms with known algorithms, including support vector machines (SVM), LR, radial basis function network (RBFN), k-nearest neighbor algorithm (KNN) and Na < ve Bayes (NB) in both prediction and selection processes. The studies show that the MCLP and KMCP models have better performance stability and can be used to effectively handle behavioral targeting application for online advertisement problems.
Publisher
SPRINGER
Issue Date
2016-07
Language
English
Article Type
Article
Keywords

CLASSIFICATION

Citation

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, v.19, no.4, pp.707 - 724

ISSN
1386-145X
DOI
10.1007/s11280-015-0353-1
URI
http://hdl.handle.net/10203/209200
Appears in Collection
MT-Journal Papers(저널논문)
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