User Behaviour Analysis and Personalized TV Content Recommendation

  • Ana Carolina RibeiroEmail author
  • Rui Frazão
  • Jorge Oliveira e Sá
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 273)


Nowadays, there are many channels and television (TV) programs available, and when the viewer is confronted with this amount of information has difficulty in deciding which wants to see. However, there are moments of the day that viewers see always the same channels or programs, that is, viewers have TV content consumption habits. The aim of this paper was to develop a recommendation system that to be able to recommend TV content considering the viewer profile, time and weekday.

For the development of this paper, were used Design Science Research (DSR) and Cross Industry Standard Process for Data Mining (CRISP-DM) methodologies. For the development of the recommendation model, two approaches were considered: a deterministic approach and a Machine Learning (ML) approach. In the ML approach, K-means algorithm was used to be possible to combine STBs with similar profiles. In the deterministic approach the behaviors of the viewers are adjusted to a profile that will allow you to identify the content you prefer. Here, recommendation system analyses viewer preferences by hour and weekday, allowing customization of the system, considering your historic, recommending what he wants to see at certain time and weekday.

ML approach was not used due to amount of data extracted and computational resources available. However, through deterministic methods it was possible to develop a TV content recommendation model considering the viewer profile, the weekday and the hour. Thus, with the results it was possible to understand which viewer profiles where the ML can be used.


Recommender systems Machine learning User behaviour analytics 



This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013 and was developed in partnership with AlticeLabs.


  1. 1.
    Cotter, P., Smyth, B.: PTV: intelligent personalised TV guides. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pp. 957–964 (2000)Google Scholar
  2. 2.
    Soares, M., Viana, P.: TV recommendation and personalization systems: integrating broadcast and video on-demand services. Adv. Electr. Comput. Eng. 14(1), 115–120 (2014)CrossRefGoogle Scholar
  3. 3.
    Peffers, K., Tuunanen, T., Rothenberger, M., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45–78 (2007)CrossRefGoogle Scholar
  4. 4.
    Negre, E.: Information and Recommender Systems. Wiley, Hoboken (2015)CrossRefGoogle Scholar
  5. 5.
    Francesco, R., Lior, R., Shapira, B.: Recommender System Handbook, 1st edn. Springer, US (2011). Scholar
  6. 6.
    Madadipouya, K., Chelliah, S.: A literature review on recommender systems algorithms, techniques and evaluations. BRAIN Broad Res. Artif. Intell. Neurosci. 8(2), 109–124 (2017)Google Scholar
  7. 7.
    Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., Lopez-Nores, M., Barragans-Martinez, B.: AVATAR : a multi-agent TV recommender system using MHP applications. In: IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 660–665 (2005)Google Scholar
  8. 8.
    Abreu, J., Nogueira, J., Becker, V., Cardoso, B.: Survey of catch-up TV and other time-shift services: a comprehensive analysis and taxonomy of linear and nonlinear television. In: Telecommunication Systems, 1st ed., pp. 57–74 (2017)Google Scholar
  9. 9.
    Oh, J., Kim, S., Kim, J., Yu, H.: When to recommend: a new issue on TV show recommendation. Inf. Sci. (Ny) 280(1), 261–274 (2014)CrossRefGoogle Scholar
  10. 10.
    Chapman, P., et al.: Crisp-DM 1.0. Cris. Consort. 76 (2000)Google Scholar
  11. 11. Welcome to H2O 3 — H2O documentation. Welcome to H2O 3 (2018). Accessed 01 July 2018
  12. 12.
    Idoine, C.J., Krensky, P., Brethenoux, E., Hare, J., Sicular, S., Vashisth, S.: Magic quadrant for data science and machine-learning platforms, no. February, pp. 1–26 (2018)Google Scholar
  13. 13.
    Zeppelin, A.: Apache Zeppelin 0.8.0 documentation. What is Apache Zeppelin? (2018). Accessed 01 July 2018
  14. 14.
    Jupyter: The Jupyter notebook — Jupyter notebook 5.5.0 documentation (2015). Accessed 01 July 2018

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.University of MinhoGuimarãesPortugal
  2. 2.University of AveiroAveiroPortugal

Personalised recommendations