Targeted Digital Signage: Technologies, Approaches and Experiences

  • Kurt SandkuhlEmail author
  • Alexander Smirnov
  • Nikolay Shilov
  • Matthias Wißotzki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11118)


Information presentation to a wide audience on large screens (digital signage) is quite popular both in publicly accessible places (shopping malls, exhibitions) and in places accessible to limited groups of people (condominiums, company offices). It can be used for both advertisement and non-commercial information delivery. Though targeted information delivery to one person (e.g., advertisement banners on Web pages) is well developed so far, targeting of digital signage is not paid sufficient attention. The paper tackles this problem from three perspectives: new technologies of interactive digital signage at elevator doors are considered, an approach to provide for targeted digital signage is developed, and new business models taking advantage of the above technologies and the targeting approach are proposed.


Digital signage Targeting Personalisation Business model Privacy 



The research was supported partly by projects funded by grants# 18-07-01201 and 18-07-01272 of the Russian Foundation for Basic Research, by the State Research no. 0073-2018-0002, and by Government of Russian Federation, Grant 08-08.


  1. 1.
    Gallacher, S., Papadopoulou, E., Abu-Shaaban, Y., Taylor, N.K., Williams, M.H.: Dynamic context-aware personalisation in a pervasive environment. Pervasive Mob. Comput. 10, 120–137 (2014)CrossRefGoogle Scholar
  2. 2.
    Anagnostopoulos, A., Broder, A.Z., Gabrilovich, E., Josifovski, V., Riedel L.: Just-in-time contextual advertising. In: 16th ACM Conference on Information and Knowledge Management, pp. 331–340 (2007)Google Scholar
  3. 3.
    Schaeffler, J.: Digital Signage: Software, Networks, Advertising, and Displays: A Primer for Understanding the Business. CRC Press, Boca Raton (2012)CrossRefGoogle Scholar
  4. 4.
    Want, R., Schilit, B.N.: Interactive digital signage. Computer 45(5), 21–24 (2012)CrossRefGoogle Scholar
  5. 5.
    Wißotzki, M., Sandkuhl, K., Smirnov, A., Kashevnik, A., Shilov, N.: Digital signage and targeted advertisement based on personal preferences and digital business models. In: 21st Conference of Open Innovations Association FRUCT, pp. 375–381 (2017)Google Scholar
  6. 6.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  7. 7.
    Yin, R.K.: Case Study Research: Design and Methods. Applied Social Research Methods Series, Third Edition, vol. 5. Sage Publications, Inc., Thousand Oaks (2002)Google Scholar
  8. 8.
    Wieringa, R., Moralı, A.: Technical action research as a validation method in information systems design science. In: Peffers, K., Rothenberger, M., Kuechler, B. (eds.) DESRIST 2012. LNCS, vol. 7286, pp. 220–238. Springer, Heidelberg (2012). Scholar
  9. 9.
    Guo, J., Liu, X., Wang, Z.: Optimized indoor positioning based on WIFI in mobile classroom project. In: 11th International Conference on Natural Computation (ICNC), pp. 1208–1212. IEEE (2015)Google Scholar
  10. 10.
    He, S., Chan, S.H.G.: Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun. Surv. Tutor. 18(1), 466–490 (2016)CrossRefGoogle Scholar
  11. 11.
    Seshadri, V., Zaruba, G.V., Huber, M.: A Bayesian sampling approach to in-door localization of wireless devices using received signal strength indication. In: Third IEEE International Conference on Pervasive Computing and Communications (PerCom) 2005, pp. 75–84 (2015)Google Scholar
  12. 12.
    Bredereck, R., Jiehua, C., Woeginger, G.J.: Are there any nicely structured preference profiles nearby? Math. Soc. Sci. 79, 61–73 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Buvaneswari, N., Bose, S.: Quantitative preference model for dynamic query personalization. Asian J. Inf. Technol. 15(24), 5019–5027 (2016)Google Scholar
  14. 14.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)CrossRefGoogle Scholar
  15. 15.
    Oroszi, A., Jung, T., Smirnov, A., Shilov, N., Kashevnik, A.: Ontology-driven codification for discrete and modular products. Int. J. Prod. Development 8(2), 162–177 (2009)CrossRefGoogle Scholar
  16. 16.
    Chen, R.C., Hendry, C.Y.H., Huang, C.Y.: A domain ontology in social networks for identifying user interest for personalized recommendations. J. Univ. Comput. Sci. 22(3), 319–339 (2016)MathSciNetGoogle Scholar
  17. 17.
    Gao, Q., Xi, S.M., Cho, Y.I.: A multi-agent personalized ontology profile based user preference profile construction method. In: 44th IEEE International Symposium on Robotics (ISR), pp. 1–4 (2013)Google Scholar
  18. 18.
    Organisciak, P., Teevan, J., Dumais, S.T., Miller, R.C., Kalai, A.T.: Matching and grokking: approaches to personalized crowdsourcing. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 4296–4302 (2015)Google Scholar
  19. 19.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  20. 20.
    Wiig, K.M.: Knowledge Management Foundations: Thinking About Thinking – How People and Organizations Create, Represent, and Use Knowledge. Schema Press, Arlington (1993)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kurt Sandkuhl
    • 1
    Email author
  • Alexander Smirnov
    • 1
    • 2
  • Nikolay Shilov
    • 2
  • Matthias Wißotzki
    • 3
  1. 1.ITMO UniversitySt. PetersburgRussia
  2. 2.SPIIRASSt. PetersburgRussia
  3. 3.Wismar UniversityWismarGermany

Personalised recommendations