Providing Customised Information Panel Content Based on User Behavioral Patterns

  • Ivo Pisarovic
  • David Prochazka
  • Dan Vybiral
  • Jana Prochazkova
Keywords: pattern recognition, clustering, location based service, user experience, user interface


Although mobile applications are commonly using user location and behavior to provide relevant content, public information panels usually lack the ability to adjust the content for a particular user or a group of users. Therefore, we focused on the development of information panels that are able, in combination with a mobile application, to collect anonymous location data about the users, identify key behavioral patterns and provide content that is relevant for the users in the panel vicinity. The key property of our solution is the anonymity of the collected information and privacy in general. The proposed algorithm consists of the data clustering and subsequent analysis. Described solution can be used in any public building or campus that the users visit regularly.


Steil, J., Muller, P.M., Sugano, Y., Bulling, A.: Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors. CoRR, 1801.06011. (2018)

Wang,Z., He,S.Y., Leung, Y.: Applying mobile phone data to travel behavior research: A literature review, Travel Behavior and Society. 11, pp. 141–155. (2018) DOI 10.1016/j.tbs.2017.02.005

Tidwell, J: Designing Interfaces, 2nd Edition, Patterns for Effective Interaction Design. O’Reilly Media (2010)

Murphy, R.S.: Property Rights in Personal Information: An Economic Defense of Privacy, Privacy, vol. 2, second edn. Taylor-Fracis Group, London (2017)

Location Based Services : Expected Trends and Technological Advancements In: Geoawesomeness (2017). [Online; accessed 25-May-2018]

Lacy, S., Watson, B.R., Riffe, D. Issues and best practices in content analysis. Journalism and Mass Communication Quarterly 92, pp. 791-811. (2015) DOI 10.1177/1077699015607338

Naab, T.K., Sehl, A.: Studies of user-generated content:A systematic review. Journalism 18 (10), pp. 1256-1273 (2016) DOI 10.1177/1464884916673557

Sjblom, M., Trhnen, M., Hamari, J.,Macey.J.: 2017. Content structure is king. Computers in Human Behavior 73 (C) 161–171. (2017) DOI 10.1016/j.chb.2017.03.036

Smock,A.D., Ellison, N.B., Lampe, D., Wohn, D.Y.: 2011. Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior 27(6), pp. 2322–2329.(2011) DOI 10.1016/j.chb.2011.07.011

Geurts, P.:Pattern Extraction for Time Series Classification. In: Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. 2001, no. 2168 , pp. 115-127. Springer Freiburg, Germany (2001) DOI 10.1007/3-540-44794-6 10.

Extracting Patterns using Neural Networks. In: Stack Overflow (2016). [Online; accessed 25-May-2018]

Song, J., Tang, E.Y., Liu, L.: User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors. In: IFIP International Conference on Network and Parallel Computing. Lecture Notes in Computer Science. 2010, pp. 177-189. Berlin, Heidelberg: Springer Berlin Heidelberg, (2010) DOI 10.1007/978-3-642-15672-4 16.

Jiang, S., Ferreira, J., Gonzalez,M.C.: Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. IEEE Transactions on Big Data 3 (2), pp. 208–219, (2017). DOI 10.1109/TBDATA.2016.2631141

Kumar, K.V., Srinivasan, R., Singh, E.B.: A feature clustering approach for dimensionality reduction and classification. Advances in Intelligent Systems and Computing 378, pp. 257–268 (2015). DOI 10.1007/978-3-319-19824-8 21

Vybiral, D., Prochazka, D.: Identification of human behavior based on analysis of cellphone location data. In: Enterprise and Competitive Environment: Conference Proceedings. 2017, pp. 940-949. Brno, Mendel University in Brno (2017)

How to Cite
PisarovicI., ProchazkaD., VybiralD. and ProchazkovaJ. 2018. Providing Customised Information Panel Content Based on User Behavioral Patterns. MENDEL. 24, 1 (Jun. 2018), 173-180. DOI: