The Application of Multiple Criteria Linear Programming in Advertisement Clicking Events Prediction

Wang, F; Zhang, P; Shang, Y; Shi, Y

The Application of Multiple Criteria Linear Programming in Advertisement Clicking Events Prediction

Wang, F Zhang, P

Shang, Y Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2013, 18 (1), pp. 1720 - 1729
Issue Date:: 2013-01

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Field	Value	Language
dc.contributor.author	Wang, F	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Shang, Y	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2013-01	en_US
dc.identifier.citation	Procedia Computer Science, 2013, 18 (1), pp. 1720 - 1729	en_US
dc.identifier.issn	1877-0509	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28901
dc.description.abstract	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 (MCLP) 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 Machines (SVM), Logistic Regression (LR), Radial Basis Function Network (RBF Network), k-Nearest Neighbour algorithm (KNN) and NaïveBayes are used as five benchmark models for comparison. The results indicate that MCLP is a promising model in behavioral targeting tasks	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2013.05.340	en_US
dc.title	The Application of Multiple Criteria Linear Programming in Advertisement Clicking Events Prediction	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	18	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	18	en_US

Abstract:

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 (MCLP) 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 Machines (SVM), Logistic Regression (LR), Radial Basis Function Network (RBF Network), k-Nearest Neighbour algorithm (KNN) and NaïveBayes are used as five benchmark models for comparison. The results indicate that MCLP is a promising model in behavioral targeting tasks

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http://hdl.handle.net/10453/28901