Targeted Digital Signage: Technologies, Approaches and Experiences
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.
KeywordsDigital 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.
- 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
- 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
- 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.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). https://doi.org/10.1007/978-3-642-29863-9_17CrossRefGoogle Scholar
- 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
- 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
- 13.Buvaneswari, N., Bose, S.: Quantitative preference model for dynamic query personalization. Asian J. Inf. Technol. 15(24), 5019–5027 (2016)Google Scholar
- 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.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
- 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