Examining the Validity of the Banner Recommendation System

  • Rong-Fuh DayEmail author
  • Chien-Ying Chou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9193)


The phenomenon of banner blindness has concerned researchers, advertisers and website publishers during these years. In order to alleviate the phenomenon, this study attempted to develop a banner recommendation system which could arrange banners according the relative salience of keywords on a webpage viewed by a user. The prototypical system are being developed, however, we have made an initial examination on the effectiveness of its banner recommendation functionality. It was found that two recommendation accuracies for the system calculated with two different criteria both were significantly higher than the probability by chance.


Banner blindness Recommendation system Eye tracking approach 



This research is sponsored by the NSC of Taiwan, grant no. 102-2410-H-260-038- and 103-2410-H-260-038 -.


  1. 1.
    Benway, J.P., Lane, D.M.: Banner Blindness: Web Searchers Often Miss ‘Obvious’ Links. Internetworking 3 (1998)Google Scholar
  2. 2.
    Hervet, G., Guérard, K., Tremblay, S., Chtourou, M.S.: Is banner blindness genuine? eye tracking internet text advertising. Appl. Cogn. Psychol. 25, 708–716 (2011)CrossRefGoogle Scholar
  3. 3.
    Dreze, X., Hussherr, F.-X.: Internet advertising: is anybody watching? J. Interact. Mark. 17, 8–23 (2003)CrossRefGoogle Scholar
  4. 4.
    Chatterjee, P.: Are unclicked ads wasted? enduring effects of banner and pop-up ad exposures on brand memory and attitudes. J. Electron. Commer. Res. 9, 51–61 (2008)Google Scholar
  5. 5.
    Li, H., Edwards, S.M., Lee, J.-H.: Measuring the intrusiveness of advertisements: scale development and validation. J. Advertising 31, 37–47 (2002)CrossRefGoogle Scholar
  6. 6.
    Cho, C.-H., Cheon, H.J.: Why do people avoid advertising on the internet? J. Advertising 33, 89–97 (2004)CrossRefGoogle Scholar
  7. 7.
    Duff, B.R.L., Faber, R.J.: Missing the mark. J. Advertising 40, 51–62 (2011)CrossRefGoogle Scholar
  8. 8.
    Baek, T.H., Morimoto, M.: Stay away from me. J. Advertising 41, 59–76 (2012)CrossRefGoogle Scholar
  9. 9.
    Kazienko, P., Adamski, M.: AdROSA—Adaptive personalization of web advertising. Inf. Sci. 177, 2269–2295 (2007)CrossRefGoogle Scholar
  10. 10.
    Todd, P., Benbasat, I.: Process tracing method in decision support systems research: exploring the black box. MISQ 11, 493–512 (1987)CrossRefGoogle Scholar
  11. 11.
    Dimoka, A., Banker, R.D., Benbasat, I., Davis, F.D., Dennis, A.R., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P., Pavlou, P.A., Müller-Putz, G.R., Riedl, R., Brocke, J.V., Weber, B.: On the use of neurophysiological tools in IS research: developing a research agenda for NeuroIS. MIS Q. 36, 679–702 (2012)Google Scholar
  12. 12.
    Lohse, G.L., Johnson, E.J.: A comparison of two process tracing methods for choice tasks. Organ. Behav. Hum. Decis. Process. 68, 28–43 (1996)CrossRefGoogle Scholar
  13. 13.
    Payne, J.W., Bettman, J.R., Johnson, E.J.: The Adaptive Decision Maker. Cambridge University Press, New York (1993)CrossRefGoogle Scholar
  14. 14.
    Just, M.A., Carpenter, P.A.: Eye fixations and cognitive processes. Cogn. Psychol. 8, 441–480 (1976)CrossRefGoogle Scholar
  15. 15.
    Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 124, 372–422 (1998)CrossRefGoogle Scholar
  16. 16.
    Rayner, K., Rotello, C.M., Stewart, A.J., Keir, J., Duffy, S.A.: Integrating text and pictorial Information: Eye movement when looking at print advertisement. J. Exp. Psychol. Appl. 7, 219–226 (2001)CrossRefGoogle Scholar
  17. 17.
    Hirsh, H., Basu, C., Davison, B.D.: Learning to personalize. Commun. ACM 43, 102–106 (2000)CrossRefGoogle Scholar
  18. 18.
    Liang, T.-P., Lai, H.-J.: Discovering user interests from web browsing behavior: an application to internet news services. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, HICSS 2002, pp. 2718–2727. IEEE (2002)Google Scholar
  19. 19.
    Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31–40. ACM (2010)Google Scholar
  20. 20.
    Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T.V., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The YouTube video recommendation system. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 293–296. ACM, Barcelona (2010)Google Scholar
  21. 21.
    Pazzani, M.J., Billsus, D.: Content-Based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Sakagami, H., Kamba, T.: Learning personal preferences on online newspaper articles from user behaviors. Comput. Netw. ISDN Syst. 29, 1447–1455 (1997)CrossRefGoogle Scholar
  23. 23.
    Billsus, D., Pazzani, M.J.: A hybrid user model for news story classification. In: Proceedings of the Seventh International Conference on User Modeling, pp. 99–108. Springer-Verlag New York, Inc., Banff (1999)Google Scholar
  24. 24.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., Gay, G.: Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM, Salvador (2005)Google Scholar
  25. 25.
    Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., Gay, G.: Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst. 25, 7 (2007)CrossRefGoogle Scholar
  26. 26.
    Just, M.A., Carpenter, P.A.: A theory of reading: from eye fixations to comprehension. Psychol. Rev. 87, 329–354 (1980)CrossRefGoogle Scholar
  27. 27.
    Rayner, K.: Eye movements in reading and information processing. Psychol. Bull. 85, 618–660 (1978)CrossRefGoogle Scholar
  28. 28.
    Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91, 276–292 (1982)CrossRefGoogle Scholar
  29. 29.
    Beatty, J., Lucero-Wagoner, B.: The Pupillary System. In: Cacioppo, J.T., Tassinary, L.G., Berntson, G. (eds.) Handbook of Psychophysiology, 2nd edn, pp. 142–162. Cambridge University, Cambridge (2000)Google Scholar
  30. 30.
    Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Remote Sens. Environ. 62, 77–89 (1997)CrossRefGoogle Scholar
  31. 31.
    Hair, J.F., Tatham, R.L., Anderson, R.E., Black, W.: Multivariate Data Analysis. Pearson Prentice Hall, Upper Saddle River (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information ManagementNational Chi-Nan UniversityNantouTaiwan, ROC

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