Forecasting Consumer Interest in New Services Using Semantic-Aware Prediction Model: The Case of YouTube Clip Popularity

  • Luka Vrdoljak
  • Vedran Podobnik
  • Gordan Jezic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7327)


With intense increase in number of competing service providers in the information and communication sector, companies must implement mechanisms for forecasting consumer interest in new services. Common growth models provide the mechanisms for modelling and predicting acceptance of a certain service. However, they have two shortcomings: i) limited precision; and ii) a short, but yet existing, time delay. By using semantic reasoning for detecting similarities between services already on a market and ones that are just to be introduced, it is possible both to increase forecasting precision and eliminate the time delay caused by the need to collect a certain amount of data about the new service before a prediction can be made. The proposed semantic-aware prediction model is elaborated on a case of forecasting YouTube clip popularity.


Consumer Relationship Management Consumer Managed Relationship Forecasting Growth Models Semantic Reasoning YouTube 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Vrdoljak, L., Bojic, I., Podobnik, V., Jezic, G., Kusek, M.: Group-oriented Services: A Shift toward Consumer-Managed Relationship in Telecom Industry. Transactions on Computational Collective Intelligence 2, 70–89 (2010)CrossRefGoogle Scholar
  2. 2.
    Sreedhar, D., Manthan, J., Ajay, P., Virendra, S.L., Udupa, N.: Customer Relationship Management and Customer Managed Relationship - Need of the hour (April 2011),
  3. 3.
    Schmitt, B.: Customer experience management: a revolutionary approach to connecting with your customers. John Wiley and Sons, Hoboken (2003)Google Scholar
  4. 4.
    Girish, P.B.: How Banks Use Customer Data to See the Future (April 2011),
  5. 5.
    Shin, N.: Strategies for Generating E-Business Returns on Investment. Idea Group Inc. (2005)Google Scholar
  6. 6.
    Rygielski, C., Wang, J.C., Yen, D.C.: Data mining techniques for customer relationship management. Technology in Society 24, 483–502 (2002)CrossRefGoogle Scholar
  7. 7.
    Vrdoljak, L.: Agent System based on Semantic Reasoning for Creating Social Networks of Telecommunication Service Users. Diploma thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia (2009)Google Scholar
  8. 8.
    Sokele, M.: Analytical Method for Forecasting of Telecommunications Service Life-Cycle Quantitative Factors. Doctoral Thesis. University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia (2009)Google Scholar
  9. 9.
    Sokele, M., Hudek, V.: Extensions of logistic growth model for the forecasting of product life cycle segments. In: Moutinho, L. (ed.) Advances in Doctoral Research in Management, vol. 1, pp. 77–106. World Scientific Publishing (2006)Google Scholar
  10. 10.
    Sokele, M.: Growth models for the forecasting of new product market adoption. Telektronikk 3, 4, 144–154 (2008)Google Scholar
  11. 11.
    Bass, F.: A new product growth for model consumer durables. Management Science 15(5), 215–227 (1969)zbMATHCrossRefGoogle Scholar
  12. 12.
    Tellabs. Forecasting the Take-up of Mobile Broadband Services. White Paper (2010)Google Scholar
  13. 13.
    Li, Y., McLean, D., Bandar, Z., O’Shea, J., Crockett, K.: Sentence Similarity Based on Semantic Nets and Corpus Statistics. IEEE Transactions on Knowledge and Data Engineering 18(8), 1138–1150 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luka Vrdoljak
    • 1
  • Vedran Podobnik
    • 2
  • Gordan Jezic
    • 2
  1. 1.Erste & Steiermärkische BankCroatia
  2. 2.Faculty of Electrical Engineering and ComputingUniversity of ZagrebCroatia

Personalised recommendations