Advertisement

Dynamic MCDA Approach to Multilevel Decision Support in Online Environment

  • Jarosław JankowskiEmail author
  • Jarosław Wątróbski
  • Paweł Ziemba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

Effective online marketing requires technologies supporting campaign planning and execution at the operational level. Changing performance over time and varying characteristics of audience require appropriate processing for multilevel decisions. The paper presents the concept of adaptation of the Multi-Criteria Decision Analysis methods (MCDA) for the needs of multilevel decision support in online environment, when planning and monitoring of advertising activity. The evaluation showed how to integrate data related to economic efficiency criteria and negative impact on the recipient towards balanced solutions with limited intrusiveness within multi-period data.

Keywords

Online marketing Intrusiveness Decision support MCDA methods 

Notes

Acknowledgments

The work was partially supported by European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 316097 [ENGINE].

References

  1. 1.
    Agrell, P.J., Wikner, J.: An MCDM framework for dynamic systems. Int. J. Prod. Econ. 45(1), 279–292 (1996)CrossRefGoogle Scholar
  2. 2.
    Amiri, A., Menon, S.: Efficient scheduling of internet banner advertisements. ACM Trans. Internet Technol. 3(4), 334–346 (2003)CrossRefGoogle Scholar
  3. 3.
    Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M.: PROMETHEE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200, 198–215 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Brans, J.P., Mareschal, B.: Promethee methods. In: Figueira, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis, pp. 163–195. Springer, Boston (2005)Google Scholar
  5. 5.
    Campanella, G., Ribeiro, R.A.: A framework for dynamic multiple-criteria decision making. Dec. Supp. Syst. 52(1), 52–60 (2011)CrossRefGoogle Scholar
  6. 6.
    Chakrabarti, D., Kumar, R., Radlinski, F., Upfal, E.: Mortal multi-armed bandits. In: Koller, D., Schuurmans, D., Bengio, Y. (eds.) Advances in Neural Information Processing Systems 21, pp. 273–280 (2008)Google Scholar
  7. 7.
    Chakrabarti, D., Agarwal, D., Josifovski, V.: Contextual advertising by combining relevance with click feedback. In: Proceeding of the 17th International Conference on World Wide Web, pp. 417–426 (2008)Google Scholar
  8. 8.
    Chen, Y., Li, K.W., He, S.: Dynamic multiple criteria decision analysis with application in emergency management assessment. In: IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 3513–3517 (2010)Google Scholar
  9. 9.
    Chickering, D.M., Heckerman, D.: Targeted advertising with inventory management. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 145–149 (2000)Google Scholar
  10. 10.
    Cookhwan, K., Sungsik, P., Kwiseok, K., Woojin, Ch.: How to select search keywords for online advertising depending on consumer involvement: An empirical investigation. Expert Syst. Appl. 39(1), 594–610 (2012)CrossRefGoogle Scholar
  11. 11.
    Deshmukh, S.C.: Preference ranking organization method of enrichment evaluation (Promethee). Int. J. Eng. Sci. Invent. 2(11), 28–34 (2013)Google Scholar
  12. 12.
    Du, H., Xu, Y.: Research on multi-objective optimization decision model of web advertising—takes recruitment advertisement as an example. Int. J. Adv. Comput. Technol. 4(10), 329–336 (2012)Google Scholar
  13. 13.
    Giuffrida, G., Reforgiato, D., Tribulato, G., Zarba, C.: A banner recommendation system based on web navigation history. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 291–296 (2011)Google Scholar
  14. 14.
    Goldstein, D.G., McAfee, R.P., Suri, S.: The cost of annoying ads. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 459–470 (2013)Google Scholar
  15. 15.
    Guitouni, A., Martel, J.M., Vincke, P.: A Framework to Choose a Discrete Multicriterion Aggregation Procedure. Defence Research Establishment Valcatier (1998)Google Scholar
  16. 16.
    Gupta, N., Khurana, U., Lee, T., Nawathe, S.: Optimizing display advertisements based on historic user trails. In: Proceedings of the ACM SIGIR, Workshop: Internet Advertising (2011)Google Scholar
  17. 17.
    Hashemkhani Zolfani, S., Maknoon, R., Zavadskas, E.K.: An introduction to Prospective Multiple Attribute Decision Making (PMADM). Technol. Econ. Develop. Econ. 22(2), 309–326 (2016)CrossRefGoogle Scholar
  18. 18.
    Jankowski, J., Ziemba, P., Wątróbski, J., Kazienko, P.: Towards the tradeoff between online marketing resources exploitation and the user experience with the use of eye tracking. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016, Part I. LNCS(LNAI), vol. 9621, pp. 330–343. Springer, Heidelberg (2016)Google Scholar
  19. 19.
    Jassbi, J.J., Ribeiro, R.A., Varela, L.R.: Dynamic MCDM with future knowledge for supplier selection. J. Dec. Syst. 23(3), 232–248 (2014)CrossRefGoogle Scholar
  20. 20.
    Kazienko, P., Adamski, M.: AdROSA - adaptive personalization of web advertising. Inf. Sci. 177(11), 2269–2295 (2007)CrossRefGoogle Scholar
  21. 21.
    Kornbluth, J.S.H.: Dynamic multi-criteria decision making. J. Multi-Criteria Dec. Anal. 1(2), 81–92 (1992)CrossRefzbMATHGoogle Scholar
  22. 22.
    Langheinrich, M., Nakamura, A., Abe, N., Kamba, T., Koseki, Y.: Unintrusive customization techniques for web advertising. Comput. Netw. 31(11–16), 1259–1272 (1999)CrossRefGoogle Scholar
  23. 23.
    Nakamura, A., Abe, N.: Improvements to the linear programming based scheduling of web advertisements. Electron. Comm. Res. 5, 75–98 (2005)CrossRefzbMATHGoogle Scholar
  24. 24.
    Podvezko, V., Podviezko, A.: Dependence of multi-criteria evaluation result on choice of preference functions and their parameters. Technol. Econ. Develop. Econ. 16(1), 143–158 (2010)CrossRefGoogle Scholar
  25. 25.
    Podvezko, V., Podviezko, A.: Use and choice of preference functions for evaluation of characteristics of socio-economical processes. In: 6th International Scientific Conference on Business and Management, pp. 1066–1071 (2010)Google Scholar
  26. 26.
    Rosenkrans, G.: The creativeness and effectiveness of online interactive rich media advertising. J. Interact. Advertising 9(2), 18–31 (2009)CrossRefGoogle Scholar
  27. 27.
    Roy, B.: Multicriteria Methodology for Decision Aiding. Springer, Dordrecht (1996)CrossRefzbMATHGoogle Scholar
  28. 28.
    Roy, B.: The outranking approach and the foundations of ELECTRE methods. In: Bana e Costa, C.A. (ed.) Readings in Multiple Criteria Decision Aid, pp. 155–183. Springer, Heidelberg (1990)Google Scholar
  29. 29.
    Teng-Kai, F., Chia-Hui, Ch.: Blogger-centric contextual advertising. Expert Syst. Appl. 38(3), 1777–1788 (2011)CrossRefGoogle Scholar
  30. 30.
    Tsai, W.H., Chou, W.Ch., Leu, J.D.: An effectiveness evaluation model for the web-based marketing of the airline industry. Expert Syst. Appl. 38(12), 15499–15516 (2011)Google Scholar
  31. 31.
    Yu, P.-L., Chen, Y.-C.: Dynamic MCDM, habitual domains and competence set analysis for effective decision making in changeable spaces. In: Greco, S., Ehrgott, M., Figueira, J.R. (eds.) Trends in Multiple Criteria Decision Analysis. International Series in Operations Research & Management Science, vol. 142, pp. 1–35. Springer, New York (2010)CrossRefGoogle Scholar
  32. 32.
    Yun, Y.Ch., Kim, K.: Processing of animation in online banner advertising: the roles of cognitive and emotional responses. J. Interact. Market. 19(4), 18–34 (2005)CrossRefGoogle Scholar
  33. 33.
    Zha, W., Wu, H.D.: The impact of online disruptive ads on users’ comprehension, evaluation of site credibility, and sentiment of intrusiveness. Am. Commun. J. 16(2), 15–28 (2014)Google Scholar
  34. 34.
    Jankowski, J., Watróbski, J., Ziemba, P.: Modeling the impact of visual components on verbal communication in online advertising. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS, vol. 9330, pp. 44–53. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24306-1_5 CrossRefGoogle Scholar
  35. 35.
    Wątróbski, J., Jankowski, J.: Guideline for MCDA method selection in production management area. In: Różewski, P., Novikov, D., Bakhtadze, N., Zaikin, O. (eds.) New Frontiers in Information and Production Systems Modelling and Analysis. ISRL, vol. 98, pp. 119–138. Springer, Switzerland (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jarosław Jankowski
    • 1
    • 2
    Email author
  • Jarosław Wątróbski
    • 1
  • Paweł Ziemba
    • 3
  1. 1.West Pomeranian University of Technology, SzczecinSzczecinPoland
  2. 2.Department of Computational IntelligenceWrocław University of TechnologyWrocławPoland
  3. 3.The Jacob of Paradyż University of Applied Sciences in Gorzów WielkopolskiGorzów WielkopolskiPoland

Personalised recommendations