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Advertisement clicking prediction by using multiple criteria mathematical programming

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Abstract

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 Naïve 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.

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References

  1. Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Int. Syst. Technol. 2 27:1-27:27, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2011)

  2. Chen, Y., Pavlov, D., John, F.: Large scale behavioral targeting. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 209-218 (2009)

  3. Chen, Y., Pavlov, D., Canny, J.F.: Behavioral targeting: the art of scaling up simple algorithms. ACM Trans. Knowl. Disc. Data 4(4), Article 17 (2010)

    Google Scholar 

  4. Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In Proceedings of the 3rd ACM international conference on Web search and data mining, pp. 351–360 (2010)

  5. Grant,M., Boyd, S.: CVX: Matlab software for disciplined convex programming. version 1.21 (2011)

  6. Guo, Q., Agichtein, E.: Ready to buy or just browsing?: detecting web searcher goals from interaction data. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 130–137 (2010)

  7. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  8. Kou, G., et al.: Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection. Inf. Sci. 179(4), 371–381 (2009)

    Article  Google Scholar 

  9. Kwak, W., Shi, Y., Cheh, J.J.: Firm bankruptcy prediction using multiple criteria linear programming data mining approach. Adv. Invest. Anal. Portf. Manag. 2, 27–49 (2006)

    Google Scholar 

  10. Kwak, W., et al.: Bankruptcy prediction for Japanese firms: using multiple criteria linear programming data mining approach. Int. J. Bus. Int. Data Min. 1(4), 401–416 (2006)

    Google Scholar 

  11. Li, J., Zhang, P.: Efficient behavioral targeting Using SVM Ensemble Indexing. Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 409-418 (2012)

  12. Niu, L., Zhao, X., Shi, Y.: A simple regularized multiple criteria linear programs for binary classification. Proc. Comput. Sci. 18, 1690–1699 (2013)

    Article  Google Scholar 

  13. Peng, Y., et al.: A multi-criteria convex quadratic programming model for credit data analysis. Decis. Support. Syst. 44(4), 1016–1030 (2008)

    Article  Google Scholar 

  14. Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, 521–530 (2007)

  15. Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. International World Wide Web Conference (WWW 2007), pp. 521-530 (2007)

  16. Schlosser, A.E., Shavitt, S., Kanfer, A.: Survey of Internet users’ attitudes toward Internet advertising. J. Interact. Mark. 13(3), 34–54 (1999)

    Article  Google Scholar 

  17. Shi, Y.: Multiple criteria optimization based data mining methods and applications: a systematic survey. Knowl. Inf. Syst. 24(3), 369–391 (2010)

    Article  Google Scholar 

  18. Shi, Y., Peng, Y., Xu, W.: Data mining via multiple criteria linear programming: applications in credit card portfolio management. Int. J. Inf. Technol. Decis. Mak. 1(1), 131–151 (2002)

    Article  MathSciNet  Google Scholar 

  19. Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J.: Optimization Based Data Mining: Theory and Applications, doi:10.1007/978-0-85729-504-0 (2011)

  20. Shi, Y., et al.: A multiple-criteria quadratic programming approach to network intrusion detection. Data Min. Knowl. Manag. Lect. Notes Comput. Sci. 3327, 145–153 (2005)

    Article  Google Scholar 

  21. Shi, Y. et al.: Data mining in credit card portfolio management: a multiple criteria decision making approach. Lecture notes in economics and mathematical systems, pp. 427–436 (2001)

  22. Vapnik, V.N.: The nature of statistical learning theory (Second Edition). Springer, New York (2000)

    Book  MATH  Google Scholar 

  23. Yu, P.L.: A class of solutions for group decision problems. Manag. Sci. 19(8), 936–946 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhang, D.L., Shi, Y., Tian, Y.J., Zhu, M.H.: A class of classification and regression methods by multi objective programming. Front. Comput. Sci. China 2(3), 192–204 (2009)

    Article  Google Scholar 

  25. Zhang, Z., Shi, Y., Gao, G.: A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis. Expert Syst. Appl. 36(5), 8932–8937 (2009)

    Article  Google Scholar 

  26. Zhao, X., Deng, W., Shi, Y.: Feature selection with attributes clustering by maximal information coefficient. In Proceedings of 1st International Conference on Information Technology and Quantitative Management, volume 17, pp. 70–79 (2013)

  27. Zhao, X., Shi, Y., Lingfeng, N.: Kernel-based simple regularized multiple criteria linear program for binary classification and regression. Int. Data Anal. 19(3) (2015). (To be appear)

  28. Zheng, J., et al.: Classification of HIV-I mediated neuronal dendritic and synaptic damage using multiple criteria linear programming. Neuroinformatics 2(3), 303–326 (2004)

    Article  Google Scholar 

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Acknowledgments

The authors thank the anonymous reviewers for helping to improve this paper. This work was partially supported by the National Nature Science Foundation of China (Grant No.70921061, 71331005), the CAS/SAFEA International Partnership Program for Creative Research Teams, International (Region) Joint Research Project (No.71110107026).

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Authors and Affiliations

  1. School of Business Administration, Hoseo University, 12 Hoseodae-gil, Dongnam-gu, Cheonan, Chungnam, 330-996, Republic of Korea

    Jongwon Lee

  2. Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management, Beijing, 100190, China

    Yong Shi & Fang Wang

  3. College of Information Sciences and Technology, University of Nebraska at Omaha, Omaha, NE, 68118, USA

    Yong Shi

  4. School of Computer Control Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China

    Fang Wang

  5. College of Business, Korea Advanced Institute Science and Technology, Dongdaemum-gu, Seoul, 130-722, Republic of Korea

    Heeseok Lee

  6. Department of Co-op, Hoseo University, Asan-si, Chungcheongnam-do, 336-795, Republic of Korea

    Heung Kee Kim

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  1. Jongwon Lee
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  2. Yong Shi
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  3. Fang Wang
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Correspondence to Yong Shi.

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Lee, J., Shi, Y., Wang, F. et al. Advertisement clicking prediction by using multiple criteria mathematical programming. World Wide Web 19, 707–724 (2016). https://doi.org/10.1007/s11280-015-0353-1

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  • DOI: https://doi.org/10.1007/s11280-015-0353-1

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