• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • User Experience for Recommendation System for Smart TV

    Author(s)
    Posoldova, Alexandra
    Oravec, Miloš
    Rozinaj, Gregor
    Liew, Alan Wee-Chung
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Posoldova, Alexandra
    Year published
    2014
    Metadata
    Show full item record
    Abstract
    This paper describes how user experience can be used to recommend program to user. Smart televisions are becoming more and more popular and together with TV content provider offer hundreds of programs to watch. One cannot have real time overview and do not want to spend time to switch channels to find something to watch. That is why we think that user can find useful to relay on recommendation and so save his time. Recommendation is based on past user experience. But it is not only statistic of watched history. This paper is dedicated to system design which is able to learn user habits and correlation between programs watched ...
    View more >
    This paper describes how user experience can be used to recommend program to user. Smart televisions are becoming more and more popular and together with TV content provider offer hundreds of programs to watch. One cannot have real time overview and do not want to spend time to switch channels to find something to watch. That is why we think that user can find useful to relay on recommendation and so save his time. Recommendation is based on past user experience. But it is not only statistic of watched history. This paper is dedicated to system design which is able to learn user habits and correlation between programs watched in past, weather, day in week and many more aspects which can affect user's tastes. We will use graphical model to explore inferences between features affecting user decisions. Bayes linear regression is used to train and predict future recommendations. Recommendation system in this paper is content based where program information can be extracted from electronic program guide. As the design is for smart TV, we can use additional information from the internet if necessary.
    View less >
    Conference Title
    Proceedings of the 8th International Workshop on Multimedia and Signal processing (Red 2014)
    Subject
    Expert Systems
    Publication URI
    http://hdl.handle.net/10072/152805
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander