Evaluation of Varying Visual Intensity and Position of a Recommendation in a Recommending Interface Towards Reducing Habituation and Improving Sales

  • Piotr SulikowskiEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)


The abundance of advertising in e-commerce results in limited user attention to marketing-related content on websites. As far as recommender systems are concerned, presenting recommendation items in a particular manner becomes equally relevant as the underlying product selection algorithms. To enhance content presentation effectiveness, marketers experiment with layout and visual intensity of website elements. The presented research investigates those aspects for a recommending interface. It uses a quantitative research methodology involving gaze tracking for implicit monitoring of human-website interaction in an experiment instrumented for a simple-structure recommending interface. The experimental results are discussed from the perspective of the attention attracted by recommended items in various areas of the website and with varying intensity, while the main goal is to provide advice on the most viable solutions.


E-commerce Recommendation system Recommending interface Eye tracking 


  1. 1.
    Castagnos, S., Jones, N., Pu, P.: Recommenders’ influence on buyers’ decision process. In: 3rd ACM Conference on Recommender Systems (RecSys 2009), pp. 361–364 (2009)Google Scholar
  2. 2.
    Allen, R.B.: User models: theory, method, and practice. Int. J. Man-Mach. Stud. 32(5), 511–543 (1990)CrossRefGoogle Scholar
  3. 3.
    Kelly, D.: Implicit feedback: using behavior to infer relevance. In: Spink, A., Cole, C. (eds.) New Directions in Cognitive Information Retrieval. The Information Retrieval Series, vol. 19, Sect. IV, pp. 169–186 (2005)Google Scholar
  4. 4.
    Sulikowski, P., Zdziebko, T., Turzyński, D., Kańtoch, E.: Human-website interaction monitoring in recommender systems. Procedia Comput. Sci. 126, 1587–1596 (2018)CrossRefGoogle Scholar
  5. 5.
    Sulikowski, P., Zdziebko, T., Turzyński, D.: Modeling online user product interest for recommender systems and ergonomics studies. Concurrency Comput.: Pract. Experience 31(22), e4301, s. 1–9 (2019).
  6. 6.
    Wątróbski, J., Jankowski, J., Karczmarczyk, A., Ziemba, P.: Integration of eye-tracking based studies into e-commerce websites evaluation process with eQual and TOPSIS methods. In: Wrycza, S., Maślankowski, J. (eds.) Proceedings of Information Systems: Research, Development, Applications, Education. 10th SIGSAND/PLAIS EuroSymposium 2017, Gdańsk, Poland, 22 September 2017, Lecture Notes in Business Information Processing, vol. 300, pp. 56–80. Springer, Cham (2017)CrossRefGoogle Scholar
  7. 7.
    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., Tawiński, B., Fujita, H., Hong, T.P. (eds.) Intelligent Information and Database Systems. 8th Asian Conference, ACIIDS 2016, Da Nang, Vietnam, 14–16 Mar 2016, Proceedings, Part I. Lecture Notes in Artificial Intelligence, vol. 9621, pp. 330-343. Springer, Berlin (2016)CrossRefGoogle Scholar
  8. 8.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  9. 9.
    Peska, L., Vojtas, P.: Estimating importance of implicit factors in e-commerce recommender systems. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, Article No. 62, New York (2012)Google Scholar
  10. 10.
    Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014)CrossRefGoogle Scholar
  11. 11.
    Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems, In: 2007 IEEE 23rd International Conference on Data Engineering Workshop, pp. 801–810. IEEE (2007)Google Scholar
  12. 12.
    Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5, 318–335 (2012)CrossRefGoogle Scholar
  13. 13.
    Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)CrossRefGoogle Scholar
  14. 14.
    He, J., Chu, W.W.: A social network-based recommender system (SNRS). In: Data Mining for Social Network Data, pp. 47–74 (2010)CrossRefGoogle Scholar
  15. 15.
    Swearingen, K., Sinha, R.: Beyond algorithms: an HCI perspective on recommender systems. In: Herlocker, J.L. (eds.) Recommender Systems, papers from the 2001 ACM SIGIR Workshop, New Orleans (2001)Google Scholar
  16. 16.
    Bortko, K., Bartków, P., Jankowski, J., Kuras, D., Sulikowski, P.: Multi-criteria evaluation of recommending interfaces towards habituation reduction and limited negative impact on user experience. Procedia Comput. Sci. 159, 2240–2248 (2019)CrossRefGoogle Scholar
  17. 17.
    Portnoy, F., Marchionini, G.: Modeling the effect of habituation on banner blindness as a function of repetition and search type: Gap analysis for future work. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems, pp. 4297–4302. ACM (2010)Google Scholar
  18. 18.
    Ha, L., McCann, K.: An integrated model of advertising clutter in offline and online media. Int. J. Advertising 27, 569–592 (2008)CrossRefGoogle Scholar
  19. 19.
    Jankowski, J., Hamari, J., Watróbski, J.: A gradual approach for maximising user conversion without compromising experience with high visual intensity website elements. Internet Res. 29, 194–217 (2019)CrossRefGoogle Scholar
  20. 20.
    Jankowski, J.: Modeling the structure of recommending interfaces with adjustable influence on users. In: Intelligent Information and Database Systems, Lecture Notes in Computer Science, vol. 7803, pp. 429–438 (2013)CrossRefGoogle Scholar
  21. 21.
    Dobrowolski, Ł., Sulikowski, P.: Prezentacja tresci w systemach rekomendacyjnych, Unpublished master’s thesis. West Pomeranian University of Technology, Szczecin, Poland (2019)Google Scholar
  22. 22.
    Nichols, D.M.: Implicit ratings and filtering. In: Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Hungary, pp. 31–36 (1997)Google Scholar
  23. 23.
    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Capturing interest through inference and visualization: ontological user profiling in recommender systems. In: Proceedings of the Second Annual Conference on Knowledge Capture (2003)Google Scholar
  24. 24.
    Zdziebko, T., Sulikowski, P.: Monitoring human website interactions for online stores. Adv. Intell. Syst. Comput. 354, 375–384 (2015)Google Scholar
  25. 25.
    Kim, K., Carroll, J.M., Rosson, M.: An empirical study of web personalization assistants: supporting end-users in web information systems. In: Proceedings of the IEEE 2002 Symposia on Human Centric Computing Languages and Environments, Arlington, USA (2002)Google Scholar
  26. 26.
    Kelly, D., Teevan, J.: Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2), 18–28 (2003)CrossRefGoogle Scholar
  27. 27.
    Avery, C., Zeckhauser, R.: Recommender systems for evaluating computer messages. Commun. ACM 40(3), 40–88 (1997)CrossRefGoogle Scholar
  28. 28.
    Liversedge, S.P., Findlay, J.M.: Saccadic eye movements and cognition. Trends Cogn. Sci. 4(1), 6–14 (2000)CrossRefGoogle Scholar
  29. 29.
    Buscher, G., Dengel, A., Biedert, R., Van Elst, L.: Attentive documents: eye tracking as implicit feedback for information retrieval and beyond. ACM Trans. Interact. Intell. Syst. 1(2), 9 (2012)CrossRefGoogle Scholar
  30. 30.
    Xu, S., Jiang, H., Lau, F.C.: User-oriented document summarization through vision-based eye-tracking. In: Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI 2009), pp. 7–16. ACM, New York (2009)Google Scholar
  31. 31.
    Ohno, T.: Eyeprint: support of document browsing with eye gaze trace. In: Proceedings of the 6th International Conference on Multimodal Interfaces (ICMI 2004), pp. 16–23. ACM, New York (2004)Google Scholar
  32. 32.
    Lee, J., Ahn, J.-H., Park, B.: The effect of repetition in internet banner ads and the moderating role of animation. Comput. Hum. Behav. 46, 202–209 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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