• DocumentCode
    3703592
  • Title

    Predicting online video engagement using clickstreams

  • Author

    Everaldo Aguiar;Saurabh Nagrecha;Nitesh V. Chawla

  • Author_Institution
    Dept. of Computer Science and Engineering, University of Notre Dame. Notre Dame, IN 46556, USA
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    10
  • Abstract
    As access to broadband continues to grow along with the now almost ubiquitous availability of mobile phones, the landscape of the e-content delivery space has never been so dynamic. To establish their position in the market, businesses are beginning to realize that understanding each of their customers´ likes and dislikes is perhaps as important as the offered content itself. Further, a number of companies are also delivering content, product previews, advertisements, etc. via video on their sites. The question remains - how effective are video engagement channels on sites? Can that user engagement be quantified? Clickstream data can furnish important insight into those questions using videos as a communication or messaging medium. To that end, focusing on a large set of web portals owned and managed by a private media company, we propose methods using these sites´ clickstream data that can be used to provide a deeper understanding of their visitors, as well as their interests and preferences. We further expand the use of this data to show that it can be effectively used to predict user engagement to video streams, quantifying that metric by means of a survival analysis assessment.
  • Keywords
    "Streaming media","Media","Companies","Predictive models","History","IP networks"
  • Publisher
    ieee
  • Conference_Titel
    Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on
  • Print_ISBN
    978-1-4673-8272-4
  • Type

    conf

  • DOI
    10.1109/DSAA.2015.7344873
  • Filename
    7344873