A Concept of Unobtrusive Method for Complementary Emotive User Profiling and Personalization for IPTV Platforms

  • Adam FlizikowskiEmail author
  • Mateusz Majewski
  • Damian Puchalski
  • Moustafa Hassnaa
  • Michał Choraś
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)


Mobile technologies, new interactive applications and the service providers’ customer-centric approach are influencing the way of assessing QoE nowadays. Traditional QoE assessment methods proved to be effective when dealing with legacy audio/video services; however, current IPTV services provide features beyond traditional TV and are not limited to delivering audiovisual content but may also rely on auxiliary services (e.g. content recommendation). Personalization mechanisms that learn instantaneous user-context relation are interesting extension of the QoE parameters enabling improved experience customization. This paper is focused on the QoE-context relation for context-aware IPTV platforms offering personalized TV experience. The latter systems are in the scope of the UP-TO-US project which is treated in this paper as a reference project dealing with user experience and IPTV. Authors define a QoE architecture for validating traditional subjective assessment methodologies (e.g. based on human visual system modeling, or standardized methodologies like ITU-T BT.500-11) by adopting additional context characteristics - user emotions. Moreover the proposed QoE module is aligned with the architecture defined in the UP-TO-US. In the proposed approach to affective QoE authors foresee important role for learning algorithms that can be applied in order to build a user model (an agent reasoning on QoE based on the gathered knowledge about user-content relation).


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Recommendation ITU-T P.10 G.100: Amd.1 (2007), New Appendix I Definition of Quality of Experience (QoE) (2006)Google Scholar
  2. 2.
    Recommendation ITU-R BT.500-12: Methodology for the subjective assessment of the quality of television picturesGoogle Scholar
  3. 3.
    Recommendation ITU-T P.910: Subjective video quality assessment methods for multimedia applications (2008)Google Scholar
  4. 4.
    Furth, B.: Handbook of Multimedia for Digital Entertainment and Arts. Springer (2009)Google Scholar
  5. 5.
    Google Tech Talks (2008), (last visited: March 25, 2011)
  6. 6.
    Tsianos, N., Germanakos, P., Lekkas, Z., Mourlas, C.: Evaluating the significance of cognitive and emotional parameters in e-learning adaptive environments. In: IADIS International Conference on Cognition and Exploratory Learning in Digital Age (2007)Google Scholar
  7. 7.
    Tkalcic, M.: Recognition and usage of emotive parameters in recommender systems. PhD Thesis, University of Ljubljana (2010)Google Scholar
  8. 8.
    Pereira, F.: A Triple User Characterization Model for Video Adaptation and Quality of Experience EvaluationGoogle Scholar
  9. 9.
    Keimel, C., Oelbaum, T., Diepold, K.: Improving the verification process of video quality metrics. In: International Workshop on Quality of Multimedia Experience, QoMEx 2009, pp. 121–126 (2009)Google Scholar
  10. 10.
    Staelens, N., Moens, S., Van den Broeck, W., Marien, I., Vermeulen, B., Lambert, P., Van de Walle, R., Demeester, P.: Assessing the perceptual influence of H.264 SVC Signal-to-Noise Ratio and temporal scalability on full length movies. In: International Workshop on Quality of Multimedia Experience, QoMEx 2009, pp. 29–34 (2009)Google Scholar
  11. 11.
    Waltl, M., Timmerer, C., Hellwagner, H.: A test-bed for quality of multimedia experience evaluation of Sensory Effects. In: International Workshop on Quality of Multimedia Experience, QoMEx 2009, pp. 145–150 (2009)Google Scholar
  12. 12.
    Moebs, S., McManis, J.: A Learner, is a Learner, is a User, is a Customer So what Exactly do you Mean by Quality of Experience? In: 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)Google Scholar
  13. 13.
    El Kaliouby, R., Robinson, P.: Mind Reading Machines: Automated Inference of Cognitive Mental States From Video. In: IEEE International Conference on Systems, Man and Cybernetics (2004)Google Scholar
  14. 14.
    Crane, E.A., Gross, M.: Motion Capture and Emotion: Affect Detection in Whole Body Movement. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) ACII 2007. LNCS, vol. 4738, pp. 95–101. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion Assessment: Arousal Evaluation Using EEG’s and Peripheral Physiological Signals. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) MRCS 2006. LNCS, vol. 4105, pp. 530–537. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Russel, J.A.: A Circumplex Model of Affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980, 2006)CrossRefGoogle Scholar
  17. 17.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International Affective Picture System (IAPS): Technical Manual and Affective Ratings. NIMH Center for the Study of Emotion and Attention (1997)Google Scholar
  18. 18.
    van Galen Last, N., van Zandbrink, H.: Emotion Detection Using EEG Analysis. Delft University of Technology (2009)Google Scholar
  19. 19.
  20. 20.
    Zawadzki, B., Strelau, J.: Formal Characteristic of Behavior The Temperament Questionnaire (FCZ-K). Faculty of Psychological Tests of the Polish Psychological Society, Warsaw (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adam Flizikowski
    • 1
    Email author
  • Mateusz Majewski
    • 2
  • Damian Puchalski
    • 2
  • Moustafa Hassnaa
    • 3
  • Michał Choraś
    • 1
  1. 1.University of Technology and Life ScienceBydgoszczPoland
  2. 2.ITTIPoznaPoland
  3. 3.France Telecom - OrangeIssy les MoulineauxFrance

Personalised recommendations