Automatic user preference learning for personalized electronic program guide applications

  • Authors:

  • Jeongyeon Lim;Sanggil Kang;Munchurl Kim

  • Affiliations:

  • Laboratory for Multimedia Computing, Communications, and Broadcasting, Information and Communications University, Yusong-Gu, Daejeon, Korea;Department of Computer Science and Computer Engineering, Inha University, Nam-gu, Incheon, South Korea;Laboratory for Multimedia Computing, Communications, and Broadcasting, Information and Communications University, Yusong-Gu, Daejeon, Korea

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2007

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Abstract

In this article, we introduce a user preference model contained in the User Interaction Tools Clause of the MPEG-7 Multimedia Description Schemes, which is described by a UserPreferences description scheme (DS) and a UsageHistory description scheme (DS). Then we propose a user preference learning algorithm by using a Bayesian network to which weighted usage history data on multimedia consumption is taken as input. Our user preference learning algorithm adopts a dynamic learning method for learning real-time changes in a user's preferences from content consumption history data by weighting these choices in time. Finally, we address a user preference-based television program recommendation system on the basis of the user preference learning algorithm and show experimental results for a large set of realistic usage-history data of watched television programs. The experimental results suggest that our automatic user reference learning method is well suited for a personalized electronic program guide (EPG) application.